Hi everybody. Welcome back to the
data science hangout. I'm Rachel. I lead customer
marketing at Posit. And and we're so excited to
have you joining us here today. If it's your first
time ever joining us, the hangout is our open space
to hear what's going on in the world of data across all
different industries, companies, get to chat about
data science leadership, and connect with many others who
are facing similar things as you. And so we do get together here
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me place. So if you're watching this as a
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in the chat so that we can all welcome you
in here and say
hello and introduce ourselves to. But we're we're all dedicated
to keeping this a friendly and welcoming space for everybody
And so we love to hear from you no matter of your years of
experience, your titles, industry, or even
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listen in if you want. Maybe you're on
your lunch break, you're out walking
your dogs or something, but also awesome to be a part of the
party that happens in the Zoom chat. So yo
u'll see there's a lot of
conversation that happens there. Links and resources shared and
just fun to meet people through the Zoom chat too. I know some people who end
up being friends from meeting in the the hangout Zoom chat But if you wanna ask questions
today and jump in, you can do so three different ways so you
can raise your hand on zoom. And I will call
on you to jump in. You could put questions in the
zoom chat and just put a little star next to it if it's
something that you want me to
read out loud instead. And then third, we have a slide link
where you can ask questions anonymously. And I am sure Curtis will go and
grab that and put that there. Thank you. We do also have a LinkedIn
group for the hangouts, which can make it easier to
find some people after the fact. If you wanna connect
with each other, I'll just share that there. As well. But as always, I like to add no
rule they have to stay on the whole time or or talk. Come and go as it
fits your schedule. Just happy to h
ave you
spending the time with us. But with that, I am so happy
to be joined by my cohost today Michael Durstein, vice
president at Wells Fargo. And Michael, to get
us started here, I'd love to have you introduce
yourself and share a bit about your role today. But also something you like
to do outside of work for fun. Oh, okay. Yeah. Well,
thanks for having me. See. I'll start with things that
I like to do outside of work. I used to be a
decent soccer player. I'm not very good anymore,
but I sti
ll like to play. So I'll play foots all
on, like, Monday nights, pretty, you know,
whenever I can get away. Nice. Let's see. Yeah. A little bit about
me or my background, professionally. I am nearing my tenth
year at Wells Fargo. And that time has been spent
not necessarily doing the same thing, but working in
the same kind of space, which is in, mortgage, in mortgage finance, so what, you know, I started
close to ten years ago as a junior analyst doing a
role that I think many people are famili
ar with. Pulling together, I mean, dozens of spreadsheets
with hundreds of tabs, all other people's models and
work that's been recycled over years and years and years
and just mazes of logic and making sure that nothing
was wrong every morning, before the the traders on
the trade desk came in and started, you know,
acting on these models, trading on these models. So there's a lot
of stress to have before six AM of every morning. So anyway, I was motivated
to make my life easier. And to make pro
cesses better. And so I, you know, I think I grasped at everything
I could to to do that, which Early on, the only corporate
approved tools that I had were Excel VBA. PowerShell. I did a lot of work
in PowerShell, which, man, after, you know,
using R and Python today, I just I think about some of the things I had to do to get tables
to work well and things like that. Anyway, yeah. And so that's really
how I started. I just I fell in love with
building processes and making tools for other people
that work really well. I think a few years
into my career, I went to I I chose to
go to graduate school. Studied applied
mathematics, specifically, like, quantitative finance, at the University of
Washington in Seattle. I did that program totally
online over three years, while I was working. And there, I was introduced
to r and c plus plus, and just immediately you know, as someone who's trying to
do table manipulation with PowerShell. When you're introduced
to r, the Oh, my lord. This this is e
verybody's
thought of this already. All the tools are already here has
this amazing open source community of tool builders. It was wonderful. I immediately
was just captured by it. So, I think when was that? That was probably
in two thousand and seventeen when I started, using our And, yeah, have
it looked back since. It really was a
major turning point. It's transformative for me. It's for my career. It allowed me to kind of
take on roles that other, I would just say typical
financial analysts
in my field, my peers, they
didn't have those skills. So I was brought into meetings, then I probably
didn't belong in. But because I could build
things that other people couldn't build as easily. So, yeah, I started working more
with kind of our quantitative modelers who are building,
statistical and other you know, kind of models to
anticipate customer behavior, in the mortgage space. I also fell into a lot
of data engineering and data scientist roles, which can be really fun. And, yeah, I thi
nk
all of that over time, just aggregated into, becoming more of a yeah. I guess official
title's vice president, but really what that means is I
just manage a a team of analysts. Of high ranking analysts. And so now my job, isn't as fun. I don't get to code near
as much as I want to. In fact, a lot of times, I'll find myself
coding in my free time because I don't get to
do it at work anymore. So that that's a personal problem
that I'm trying to figure out. And work through. But instead I have a
team
of of analysts that are excited now to kind
of learn new things, solve problems. And so that's it's
it's it's new for me to be in a coaching role
and to work on problems with people to try to, yeah, untangle a knot or
put together a puzzle, It's it's it is fun. It's
it's fun in a different way. So, yeah, that's my role today. And then when I'm not
actively working on problems, I am usually on calls
advocating for analysts advocating for projects or
for ways to build things and trying to co
nvince
the directors and the executives above me
to, to make more, or to make certain strategic
and tactical decisions. Anyway, I know that was
long winded, but thank you. No. That was that
was great. Thank you. I I loved when you were telling
me that story about like, you having to go in to work
at five AM and, like, put together these spreadsheets
because I could just, like, picture that and thinking, like, there must be a better way. But what was your, like, final
breaking point with that, li
ke, where you turned
to data science? So I think honestly, when I I'd I'd moved
past that role, really. That was really an intro
role in my group to kinda put new analysts through
the gauntlet a little bit. So I moved past that role a
few and after a few years, I'd gained some of these skills
in r and Python and whatnot. I kinda looked back, and I'm
like, we should not have that. That should not I don't care
if it's a right of passage. That's a mess, and
it's operational risk. Because I was real
ly tired
and I could have easily broken something that would have
cost the bank some, you know, financially if a trader trades
on bad decisions and they have to unwind that, you you're
opening up the company to a loss. So, it was not popular because often it's don't fix
something that's that broken. That's that was the mentality. And so slowly, I've worked to build
challenger processes. I mean, I feel like the only way
to get something done in my company is you have to build
something in paralle
l. It it operates on its own.
It's a competing product. And then you have to run
it side by side and absolutely obliterate the old competition. You have to show why
this is so much better. It's tell the story
to your bosses. And so we were able to do that. The program that runs today that
does what I used to do ten years ago, runs completely an r. It runs on a server on
a Linux box every night. And it's completely automated. There's no junior analyst
that has to do it. Nobody has to wake up at f
ive
AM or or really at four thirty, get at the office five. Yeah. I mean, those junior
analyst roles still exist, but now we're encouraging
them to learn r. Or to try new things or
to do data science. Right? They're not just filling out
spreadsheets or making sure that this is green and this
is red and It's it's just a different role in that. And so, yeah, the, honestly, the R program just to
give a little bit more detail, it it's it sits on that server
and it waits for upstream models and other
calculations to be finished. So it's just it's
just a waiting. It's, you know, outcomes and results, and
it aggregates that information, calculates downstream things,
and and pushes those results into a, into a cloud
database, basically. But then all of our visualizations
and reports are built on. So I'm very proud
of that, but it was, is a lot of work technically
and maybe even more work politically to get that,
to get that through. Such an awesome
example. Thank you. Liz, I see you had asked
a question in the chat. Do you wanna jump in here? Sure. Thank you for
sharing all of that. I think we can all kind of identify
with that to a certain extent. And in my role as a data
site, as a data analyst, do we also had to push really hard
to get our stuff on the cloud? So But my question is a
multi part question, so feel free to ignore
some of it if you want. So it's is your current
toolbox limited to r in Python? And what are your feelings
if any on VS code and get co pilot. And in your cu
rrent role, how knowledgeable do
you need to remain on on the rapidly developing
data science landscape? Yeah. That's a great question. So I as far as my
company specifically, We are trying to, yeah, upskill,
whatever that means. As much as possible. We have a lot of very talented
financial analysts with business and knowledge, and they're usually all great
or think they're great with Excel. We're trying to teach
Python across the company. I've been a voice. I have there's a few other
people tha
t say, Really, this should be multilingual. Right? R is a great tool. A lot of analysts find it
more accessible to start. Or even long term. You know, there's certain problems
that I prefer are to to solve problem. There's certain problems that
Python could be better here. Right? You know, or even people
that just have really pure sequel, skills, mean, there's new
technology now like DuckDB, where you can really write
pure sequel and connect it to Python, connect it to r. I mean, it's it's it's
so
exciting now how easy it is to connect processes together. So no, I don't think we're necessarily
limited to just those boxes. But as a company, we are trying intentionally to
limit the scope of what tools are we gonna use to solve
problems because we have to manage it somehow. Right? I think also from
my, like, boss's several layers up perspective,
they don't really understand how effective good
change management can be. So git and GitHub. And if you really are using
these tools effectively,
how you can control,
you know, source. So, yeah, I think Let me let
me backtrack and make sure I'm answering your questions. Yeah. I I do think we're
trying to be multifaceted in what we're using. Try to using the try to use
the best tool for the job. V s code. I love v s code. I use it for Python. I use it for r sometimes,
especially if I have a project that requires both. Even though, I mean, I see that our studio,
you can you can run, Python pretty effectively
using reticulate. Yeah. That's
pretty slick too. I think most of the analysts
on my team will use, VS code for Python
or Python for Python. We don't we are not allowed to
use co pilot yet as a company. That's that's just, that's just policy
until somebody says it's allowed. Great. Thank you. Yeah. Have ERC had a question in the
chat when I jump in here next? Yeah. Thanks, Rachel. Michael, I've loved
your comments so far. Your background time's awesome
as does the work you do. But, I'm no longer in finance, but
I started my ca
reer in finance. And I was doing more like M and
A and valuation type or so so maybe less aligned with the
quant finance work per se, or like, you know, financing. Like, that's definitely not
something I was doing, but anyway, I still keep up with
some of the chatter. And over the years, I've seen
a trend of, like, you know, more finance professionals
talking about data science, whether it's like use cases with R
or Python or whatever it might be. And, Just today, I was seeing this
long kind of
discussion thread on LinkedIn about how quant finance managers and banking
finance managers have had a hard time hiring
data scientists and putting them into you know, the capacity of like a
quant for lack of better words. I'm using that term
pretty loosely here. But, yeah, I mean, it was
a bit surprising to me. And, you know, I'm I'm
out of that world now, but I'm just curious if that's,
like, a sentiment you share or, you know, what maybe your
perspectives are around that, like, Do you feel li
ke some
of the data scientists that might excel at r or
excel at Python actually lack, like, the math chops to
really succeed in those roles. I don't know. Yeah. Open
ended question here. No. No. I think that
that's a great question. And I think the
short answer is yes. It's been challenging, but maybe the reason
why it might be unique, So again, I I work
at Wells Fargo, which I don't think it's any, any secret that the last seven years
have been difficult for this company. Because of, sales pra
ctices that were
happening on the retail side of things, where, basically, we we're opening false credit card, accounts for individuals, to basically increase sales
metrics So the fed federal reserve and the federal government
put an asset cap on this company. And forced us to really clean up
our governance and things like that. So having said that it's been
difficult to hire anybody for seven years. So Yeah. I'll start with that. But but secondly, I do think
there is some middle space that is n
eeded between or or or really what where
we had a roster of financial analysts before that were
really savvy and good with cash flow models, discounting
valuation models, things like that. We're trying to build an in
house team here with with our and Python skills. We we want them to be
doing that same kind of work same kind of analysis, but
with a new tool set. So that that's really the role that
I'm in to day is I have a roster of analysts that, they're pretty pretty
technically savvy. But we'
re supposed to be going
outbound and teaching others. Teaching is a is a core
part of what our team does. But hiring, hiring's been
tough just as a as a company. I I'm not sure exactly how
other banks are handling that middle ground right
now where you want upscilling, like I said. I don't know if that was good. Appreciate it. But No. No. That that that's good. Yeah. It was a open ended
question anyway. And then just just a quick
follow-up, do you see in banking, that there's a space for, let's
say, individual contributors to keep
moving up in their careers, or do you feel like it's more
of your you kind of need to go into management or, you
know, or you're out? I hope so. I hope so. At Wells, I know we have a program
that that we're trying to do that. We're trying to
really incentivize rock star stole contributors, to to stay doing what they do. Great. Right? Building processes,
building tools, Yeah. I think management, so far, it has its
ups and downs to speak in cliches. But also I
think part of
my job isn't just managing. It's not just being a
manager not doing the work. I think part of
the reason why I'm kind of put in this
position is because I work hard to influence people. I I really I try to, partner with other teams
that aren't you know, on an org chart
direct, you know, directly under me. They have no reason
to help me. Right? Besides the fact that I've
helped them in the past. Or that we've worked on
projects in the past, and we think alike, and we
like working to
gether. We like solving
problems together. So, you know, whether that's a
formal manager or not or a sole contributor I think really
what's most important to organizations is having people that
are facilitators that are connectors. Right? Because at a certain
point in the bank, or probably any any big company technical skill
doesn't scale anymore. Right? Like, I'm at a point right
now where I have thirty or forty hard problems that
I'm being asked to solve. And I they're all
interested. They're
all flow. A lot of them are interested.
They're not all interested. But I wish I could spend a
couple days each chewing on each problem and finding
out a way to solve it. I can't. I can't.
Don't have enough time. So one, I have to coach
people that are also very talented and curious on how
to solve these problems. And not every solution is how I'd
wanna do it, but that, you know, there's a there's a cost
benefit analysis that you have to perform there. And for jobs that my team can't
do, yeah, I
have to go outbound. I have to figure out who
can do this in the company? So, yeah, I know
that, it's maybe a, a case for the management route. And maybe that maybe I'm just, explaining why I
sold my soul, but, but, yeah, that's that's
kinda how I see it. I I love this the focus on
relationships here in that, like, facilitation role as well. I think Adam, this actually might this is
a great time I think for your question you asked
a bit earlier. Do you wanna jump in here? Yeah. That was a perfe
ct
tie in. Thank you. Because I'm I'm and, like,
thinking about that political piece, the relationships, how we work with business
partners and, like, what the role of data science
really is with respect to the business where, you know, our goal
is at the end of the day to, like, to sell stuff and I see data
science as like one tool that fits into this
big broad picture. I'm wondering
about, like, I mean, you mentioned the the bridge
building and the connections. Do you have besides
that, do lik
e, do you have a framework for
building those relationships and getting buy in,
do you have, like, quantitative frameworks
for showing value of it, of an initiative for
it's implemented, like, how you think about
that kind of stuff? No. I think short answer is I could
be I could be better at at that. So my boss is a story tell. He he loves he is he's
great with people. He he will he can go on and
he got a little flamboyant. And so he likes to
think in in stories. As far as really technical data
science, like, he's just that doesn't it doesn't
register with it. As well. So I think in my personal world, I've been working for the
same guy for a while. Too. So a lot of my work has
to also flow that way. I will I will
interpret the metrics. I will try to understand what you know, direction,
technically, we need to go, what where we need
to focus energy, these these kind of things,
or what what we need to build, but then going up I have
to make that into a story. And usually it's
verbal or w
ritten. His boss is the exact same way. So that's that's kind
of the the really analytics part usually stops or
or stops around me. That's not the
case for everyone. I I know some executives
that are very number driven. They need they need to see the
actual and it's usually because they had a background like ours. So yeah. Short answer, not for my boss. Yeah. I mean, I can relate to that because
I know a number of projects that I've worked on that have failed. It's often because I'm trying
to ge
t too quantitative. I'm trying to get
too in the weeds. I'm not telling that
that narrative arc So that's very interesting. I think it's so hard because
when you build something that's fantastic, you fall
in love with it. Right? Like, you you
know that this is great. This is fantastic. Tastic. You need to see how
hard this was to solve. Right? And I just try to
humble myself and remember how busy my bosses are. And how mint like, if
I have forty projects, they have two hundred projects. Right? S
o what how do you
capture their imagination? And, unfortunately, it's not how cool of a
problem this was that you saw. It's What is this
going to do for them? How is this going to
make this place better? Their life easier,
that kind of thing. And so, yeah, unfortunately,
I think it is, like, putting on your, you know, best Don Draper face in
in selling it, you know. I don't know. Yeah. No well said.
Thank you. Awesome. I I love that. We've been talking a lot about, Madman presentations
internall
y, like, having those big
picture presentations. Sure. I'm gonna jump over
to, Jesse's question. Oh, I see a star
here, so I'll read it. And let any let me know if my
wifi is getting a little spotty. Am I okay? So far so good. Okay. People got okay. People got choppy for
me for one second, but The question from Jesse was
what are some problems that you see are being more useful
for tackling and vice versa? What are some problems that you
see Python being more useful? For? That's a great question
. So personally, I find
r to be my favorite language to program
in because don't know. It may be I I think
specifically kind of the tidy verse, dialects. When I have to work
with tabular data, and I really need to take
multiple sources of data and you know, transform into information. It's like gardening. I know that's I know other
people have said that. But it really is, something that I find
to be kind of tranquil. So when I think of pure, yeah,
data transformation, tabular data transformation
,
r is my good and I think it will be and
I and I know Python has counterparts like, you know,
whether you use pandas or pullers. Pullers is not popular. Great. Fantastic. I enjoy writing in our tidy verse
more than I like writing pullers. So I think there's there's a
wonderful thing about today is that preferences
can be, you know, your preference is great. It it's it's perfectly
fine. We can work with it. As far as Python, what I have
found Python to be really better for or just
personally per
sonal opinion is when I have to work
if I did do something that's object oriented, I I,
I tried I usually use Python to create classes
and things like that. I know r is some of that
functionality as well. I just think Python
is a little cleaner. Also, when I have to so we
orchestrate our processes using Daxter now. And so that's a Python based, orchestration tool. And so we we will package
individual tasks, right, that have to happen,
individual transformations. Those can be an r or
Python. Does
n't matter. We'll either write the
parquet and trans, you know, transfer between the two
languages, or we will, use duct DB, for example, I brought it up earlier
to kinda create a little mini database that works
with both Python and R just fine. So that's kinda how we
will, you know, transmit data from language to language
in the same flow. Yeah. Thank you. Morgan. Morgan, I see you
had a question around AI. Do you wanna jump in? Sure. Thanks, Rachel. So my question was around AI. I feel like it
's all
over the place right now. You know, it's it's kind of a big
push for everybody to spin up and become AI professionals as
fast as we learn what AI is and as fast as the
GPT models being created, what are you doing in your
office to kind of help your existing team scale up, well, scale up and and
meet that demand? Yeah. So, like, like I said
earlier, I I think we are slow to the party
because as a company, we're not allowing analysts
to use AI assistance yet. I don't think that's far away.
Because just in personal usage, I find it to be
incredibly helpful. I find the answers that I
get out of LLM models to often be wrong or imprecise. But often helpful. Right? It gets me closer, and then
I can iterate. Okay. No. That's not the right
question to ask. Usually, by the time I
get to the right question, I understand what the answer is. It's funny how that works,
right, where you're like, oh, my question was wrong. And so you're kind
of iterating on that. So I do think that
for senior o
r even for skilled analysts, That's gonna be an incredibly
valuable tool that's gonna accelerate development time. You know, maybe not some of these
people are like sixty, seventy No. But maybe thirty percent, twenty
percent faster to to solve problems. I just don't understand how anybody
can leave that on off the table. But, yeah, now for a
junior analyst, Right? Like, when I was starting
rewind ten years ago, I don't know if I would have
known what to do with it. You know? Like, I just I would
n't know
at all what questions they ask. So in my head, I I haven't
mapped out how people growing alongside these
models how that exactly is gonna play out yet. Yeah. Thank you. Yeah. And and that I feel like
that makes a lot of sense. So thank you. We're kind of in a little bit
of a similar vote in my office where some tools are allowed, some aren't But
at the same time, there's sort of a
need to have people understand all of the
principles behind the tools that we can't use yet. So it's it's s
ort
of an interesting, interesting way to believe
that we're learning. And thank you for for your time. You know, but Yeah. Absolutely. That's gonna be one of the trickiest
part about parts about this. Right? It's policy, policy around these models. Totally undefined yet in my in
my company. I'm sure you know. I see Bill had a question. There's a star next to
it, so I'll read it. But it was in partnering
with other teams, are you on-site where you can meet
at a water cooler or coffee sheen, how
do you network internally
in the age of remote work? Very difficult. And people don't
agree on on what's what's happening here. So we we come into the
office three days a week. I'm in Saint Louis. I live twenty five
minutes from the office, and Saint Louis is a
pretty easily commune pretty easy, trip, for me. However, I work with people that are
in Charlotte with people that are in New York City. So I have a little bit of both, where I can, yeah, meet
with people in in person, really rely on tha
t person
to person communication, to get things done, but I also have
partners that are totally remote. That is a I I don't think
there's a perfect solution to communication today. We learned that during COVID
where we had to quickly learn how to do everything
a different way. And now the problem
is almost harder. Because it's not one way
or the other. It's both. Everybody's trying to do both. And I think that just makes manager
middle manager's job way harder. How do you communicate with
people
in the best way possible for the specific task you're trying to do. Right? So, what I'm trying to
do with my team is, yeah, it really depends on what the
purpose of the communication is. If we have to solve a problem and it's challenging, I would prefer to be in person
with an actual whiteboard. I really would. We're gonna
lock ourselves into a room. We're gonna try to solve it. If you can't be there
in person, yeah, we're gonna do everything we can
to get you there virtually as well. We'll get
you hooked up with
you know, a shared screen, shared whiteboard,
things like that. I mean, I'm lucky that at this
company, we have and those resources that we can use those. But I think they're pretty readily
available in most most places. So, yeah, I I just I think the interaction has become harder. And I just it's gonna
take time to to solve it, and you had to be more
thoughtful about it. Thinking about your
past ten years at the company, so going from junior analyst
to your role now as a vic
e president. Were there any valuable
lessons you learned along the way in progressing
through the company. I know we touch on the
relationships piece, but many any others
that come to mind. Yeah. I think probably the one
of the larger things is, is patience is, understanding that when you
work at a company of this size, things take time. Momentum has to be built
over time relationships have to be sometimes built over time. I am a just serial overpromiser or or I
promise things too soon. And then
I have to then I have
to explain why they're not there when I've said
they were gonna be there. Because for me, I I
think I just, I'm a little bit of an optimist of that
will be done in three months. And, you know, four five
months later, you know, the project's still not done. So, just yeah, being more pragmatic about things,
being more patient. That that's probably
been the biggest, personal thing that I've
had to grow into over time. Thank you. Sunday, I I see you had a
question around like
pivoting between industries. Do you wanna jump in here? Sure. Yeah. Great talk so far.
Are we doing good? And, yeah, I was just
thinking, in the past, I've I've kind of thought
about it a couple of years ago, and I've been in
health all my life. And I've always wondered, what
is it like crossing over to, you know, finance. Is is it more niches? Is
there something special? You know, what's it like? Or is it simply once I get to know
the data and I see what data is. It's all the same.
It's generic
anyway. You know, you'll learn. And have you had to work
with people like that? Well, you know,
people that you know, from health or some
water industry, and how do they perform? How how do you do? Yeah. So I wanna flip, you know, I'd almost ask you
the same question. St. Louis is kind of
health care, however, at least we like to think
we are or we have a, a university of Washington
here, which is a big, medical school. And I've thought, you know, all these
people are right there, data science.
It's it's kinda the
skill set is portable. Honestly, I would take anybody who has
a problem solving mindset familiarity with some of
these open source tools. I wouldn't care if you knew
anything about a financial model. I would want you on my team. That's the state of
where we are today. That's kind of, answering
an early question too, probably better than
how I tried before about yeah. There being a gap in in skills, in our industry. Yeah. Because for finance, at
least where where I am, they'r
e just We we just have to
model it's statistical model. So it's it's, you
know, whether it's predicting prepayments,
of of mortgages. Right? If a customer is going to
refinance on their mortgage or not, we need some statistical
confidence that they are or they aren't based on how
interest rates have changed over the last six months. Right? Because we have
to make you know, we have to keep the bank
running can't if everybody refinance their
mortgage tomorrow, these these serious issues. So, yeah,
models, yeah, data exploring just data and being
able to tell data stories. I I do. I think those skill
sets are totally portable. Awesome. Thanks. Thinking about that example
you just you just shared, like, doing an analysis for the
prepayment of of mortgage. How do you how do you
go about that use case? Like, how do you
share that with business stakeholders? Kidding. No. They they are. There's I mean, it's it's
a it's a model with, like, hundreds of nodes. It's it's a pretty
sophisticated bea
st. Well, I guess, I mean, like, how do how do you expose that
model to others across the company, or how do
people interact with it? Oh, okay. So, yeah, there we had that
model is written in c plus plus. It's got an API. So we have several
models that kinda for for our, like,
models that we use, tenant, what I would
say in in production, or they are used every
day or every month, and they kind of feed other
downstream downstream reporting or they feed databases. We usually try to use compiled
l
anguages for, for that work. Doesn't have to be, but that's
just kind of the culture here. We'll write that in c sharp
or c plus plus, normally. So our prepayment and default
models are written in c plus plus. They feed a cash flow
engine, an evaluation engine that was originally written in r,
but now it's in c sharp. And then those that data is
written to a SQL database. Right? Now any reporting logic that we
wanna do or data analysis, we we like to keep that flexible. Because that changes
freq
uently. Right? The models hopefully don't
change very frequently, just the inputs to
the models change. Or model assumptions can change, but the model logic themselves, just for governance purposes
alone, should stay pretty, Yeah. Lock down. It's a it's a
whole circus to get internal governance teams
to sign off a model changes. It's it sucks. But anyway, our reporting logic,
data analytics logic, we want that in R, we
want that in Python. And other loose,
more flexible things. And we manage tha
t,
like, reporting code. We use GitHub, like enterprise
GitHub to manage all that. Yeah. Well, thank you. Jacob, I see you just asked
something in the chat. Wanna jump in here. Yeah. Hey, Michael. I'm just wondering if
you guys at Wells Fargo, share any of your data science
or client use cases online. Do you have any any
sort of repository where you share, projects that have, gone
over well with the company? So I have not personally
and I don't I don't know. I I would say my just
instinct is is
that we don't. That's that's I wish the
answer was different. Mhmm. Yeah. Okay. Thanks. I know you just mentioned GitHub
and Rick had a question earlier. I know he had to
leave for a meeting, but I'll ask it
for the recording. But to what degree does your
team use GitHub and Rick said and My experience data science
folks tend to be intimidate intimidated by the complexity
of GitHub to the point where some consider it a
specialty onto itself. Yeah. I do I do too. I I
consider it its own skill. An
d it was it was hard for me to learn I it was not, you
know, native really. It didn't click right away. It took a lot of time
of brute force learning. And so that's what
I tell my team. I'm like, this this
is gonna be tough. I know that it's tough. Force force it. Force
it. Keep forcing it. We can do it wrong.
It doesn't matter. But let's let's keep practicing
and get so that we can I would say we've done that for
about six months now, and the team now loves it? It is working fantastic. So I I t
otally agree that
the upfront cost, learning, it's intimidating. It's difficult. But, I mean, man. Once now that we have it
and we're branching off work and borrowing and re merging. And, it it's it's so slick. Yeah. I just It's like working out. We just gotta sometimes do
it even when it's not fun. Definitely. But let me just double check
that there weren't some questions that I'm
missed earlier. There was a specific
question around shiny, and it maybe was
a little bit more detailed, but I so m
aybe I'll ask first, do you use shiny and
I have in the past. I've used shiny in the past. And, right now, the powers above
me at probably the executive level hear about
power BI all the time. Right? And think that that's
good the same thing. It's hard for me to communicate
up order has been so far to say that, that they're not the same thing. That I'm a lot I'm able
to more dynamically put models into shiny than
I can into power BI, right, where I can look at
different models and different vers
ions of models and dynamically
produced results, that have kind of sophisticated
calculations and things like that. So that's a that's something that
I'm continuing to advocate for. Luckily, I've I've had some
economists that I work with in the company, that love
r and love shiny. And so they a long time
ago got, like, a Linux box, and they host a bunch of shiny reporting on there
for their kind of, economic research. I'll I'll borrow that from
them every once in a while. Some space there. I I t
hink it should be one of
the resources that any kind of mature operation has. It's just especially now that
Python plugs right into it. It's great. Awesome. Thank you. Well, a question I always
like to ask on the hangouts, and maybe one day I'm gonna
actually go and take all of these little clips
and put them together. I I really wanna do that, but
is there a piece of career advice that you've received
over the course of your career or maybe a piece of career
advice you give others that you'd li
ke to share with us? Oh, man. I don't know. I feel like my
spiel earlier about trying to be
collaborative because eventually technical skill
doesn't scale anymore. I feel like that is
kind of my really That's my frame of
thinking right now. And then I've received
a lot of good advice. And not not always
just even verbal. Sometimes it's just
consistent support. Coverage. I've had good bosses that
have given me coverage to think about problems, not feel rushed to
come to a conclusion. And so I I t
ry to play pass that
forward, pass that on to my team. I don't want them to
feel any pressure. So so if people find
themselves in kind of a manager role or even a VP
role or a director role, whatever. I would say the best thing that
you can do is try to provide some shelter for your
team to take their time to to to find a better solution. Maybe not just the
first solution, because I I think it pays
dividends in the end. So I don't I don't
know if that is a great response, but that's
that's what
I have on my head. Oh, that's great. Thank you. I I really like when
you were describing, earlier in in our
hangout today about When you're trying to
change the status quo, like building something
input in parallel, and I thought that's, like,
it's a really nice approach. To that rather than
saying like, hey, we have to replace
this right away. And I was just wondering,
like, what's the what's the next step there? So you build it in parallel and
then you just tell somebody, hey, this is a better
way of
doing it, like, check this out, or what are the steps I think
it depends on who your cuss who who's the customer? Who's who's gonna use it? Who's gonna benefit from it? If it's somebody that built
this legacy process over years and this is their baby, the
best approach is not to go, hey, this is way better. You have to use this. This
is gonna crush your thing. No. For that, I have
to bring them along. It's gotta be a little
bit of inception. Right? You gotta you gotta teach
them a little
bit about it. Kind of bring them to water. That's that's how I found to be
most successful in that place. I think if it is a boss or a manager
and they're really more result oriented, then I think that's the
language that you had to speak. Listen, you have a team of people
that are dedicated to building this one report because it's so
complicated, and it You know, if someone gets sick, you
have to have coverage on. Well, this thing that I
built, it runs automatically. It has three inputs. You k
now, and it it links
to this, this, and this, and it's all trapped. Inversion controlled. And blah, blah, blah, you
can you can spin that story pretty easily to the
people out there, and they've usually after
the first thirty seconds, they've already decided. Right? And so there are
questions who say, okay. How do you sunset the old thing and
how do you replace the new thing? You gotta communicate to anybody
that touches this thing that Hey, it's going away. It's
going away in in a quarter. And
here's the new thing.
You can find it here. Right? Yeah. That's that's how
we've we've done it. Thank you. I also wanted to dig a little
bit deeper into the coaching that you brought up
in the beginning too. So you said it gets a little bit
newer to be in this coaching role. So are there some
lessons learned so far in that coaching role or maybe
even books that you'd recommend for some of us who
are new to that? Oh, no. No. Unfortunately, I
think all my coaching, for better or for worse. For sur
e, is from, you
know, playing sports. And then also, I I used
to do, like, improv a lot. And so, like, that
collaboration and teamwork and trying to so, I feel like that's what I try
to pull from is that background. I'm I probably should read
more about managing people. So guilty. However, lessons, I think I I have learned to
let go a little bit. I think a lot of people that come
from a quantitative data science role. They've spent so many
time, solve so much time solving problems, thinking
thro
ugh how best to do things, how to organize things. I think we can be
control freaks sometimes. And I am very guilty of that. And so learning to let go and
learning to let other people make that call and really
just trusting that. It's good. And and taking the result and
moving on, you know, take the win and move on Don't be
a stickler on on things. Don't burn a bridge for
something that's worthless. You know, it's
just a preference. Yeah. That's that's something that, has had to be learned. Woul
d you ma would you
recommend improv to us for some data storytelling? I would recommend
improv to anyone. It's been a long time
since I've done that. But, man, it just forcing yourself to
be a little bit more open, a little more accepting. Listening, a lot of listening. Yeah. How did you get into that? Oh, I had a friend in
high school that probably said it was fun. And so I did, and then I did it through
college and through my twenties. Yeah. So thinking about, I guess, as we get to the end of
they
hang out here, thinking about, like, the next year
ahead for your team, Is there something that
you're really excited about? Oh, man. I'm very
excited, about this year. We already have
so much going on. I mean, I I'm talking about a lot
of these things what we're working on with GitHub and, are
using GitHub and we've been given license by my boss and
my boss's boss to really, take take the gloves off and
just start chopping up processes. So don't get me wrong. I'm still trying to be
polite
when telling people, hey, This is a fantastic
beautiful process that you built over time. We're going to destroy it. No, or really, we're just
going to change it. Right? We've been given license to do
that in a lot of different places. So for me, yeah,
all of a sudden, five problems became
forty problems. Right? Now I have to have my team really
churning and they're getting there. They're real like, the last
year was coaching, coaching, teaching, teaching. And I'm starting to
really see it pay o
ff, and it's very proud of it. I'm very excited about
it because you know, my team is now doing things
without me and solving things. And I'm I'm receiving now ready
answers instead of having to jump into it with them. So it's fun and we're just, you know, I I don't need to get
into detail, but it's you know, forecasting daily interest
rate risk management, all of these legacy processes that are
pretty fundamental to our business. Are getting a total overhaul. Pretty fun. A bit. I do I know we h
ave five minutes
here, so I wanna make sure. Were there any questions
that I miss from anybody I know there's a lot
of great conversation happening in the chat, so sometimes
we miss a few. Feel free to raise your hand
here or just copy and paste it again into the chat. But the one that I will ask first,
I I get to ask my oh, there's James. Okay. James, you had a
question I think I missed. Do you wanna jump in? Sure. So I had a question. I noticed that you solve a
lot of different proms within, t
he quantitative finance side. What type of decision
frameworks do you use? I mean, I'm hearing, you know, a lot of business stakeholders
gravitating to purely just descriptive analytics for a lot
of their business reasoning, but, I understand that
typically you and the quantitative, field, you would have to use a
lot of financial modeling, helping them understand metrics are very, very cumbersome and and difficult
for business stakeholders. How do you bridge that gap
There are people that like t
o make decisions based on, simple metrics to what you're
trying to enunciate in your field. Yeah. And I think that's why
data analysts are needed, you know, to to be able
to understand complexity, simplify, simplify. It it's it's such a critical element
of what what I do, what we do. Yeah, we use a lot of
quantitative models, but it is, like, I use the
prepayment default models, We also have interest rate risk
models where we're calculating all kinds of, Greek degree. I don't know who's familiar
with, you know, delta gamma, theta, row, all these things that
the traders use to make to to, you know, purchase swaps
and swaptions and all kinds of. So it's all very complicated. And how do I communicate that
to an executive who does not understand any of that? Yeah. It's active work. There's
no automation there, so far. So I'm sorry. I'm I'm
there's a two part of this. As you're dealing with
residuals and understanding your model is not gonna
be perfect initially. How do you get buy in for t
hem
to understand that, you know, this is a a learning
process for your model and, from a resort Sure. A result oriented perspective, how do you get them to
understand that confidence intervals take time
and, if you are very time sensitive in your your,
I guess responsibilities, do you explain that
this model may not work? And we may need to
pivot to something. And what do you do in
that decision framework? No. Okay. I understand
your question better now. You're thinking more from
a modeler's pe
rspective. So I work with
the the quantitative modelers. So, yeah, their actual research and
development of new models or updates to existing models,
that is a rigorous process. And I don't think there's a one I don't know. I I would phone a friend if
this were a company call. And ask one of them, a
couple of my favorite ones. But, yeah, we at a
big company like this, you have model
governance program, you have all of
these these layers of of red tape that you have
to go through anyway. So you c
an't push a model in
with any expediency, really. It's it's really it's
really difficult. So you have that runway
to develop and build and, yeah. That that's a tough role. That's a tough role
that they have. Can we ask one? I know I didn't
answer your question. Yeah. I I'm sorry. Yeah. It's because when,
you know, you may be using tons of different models
and you've come to varied minuscule results
where, you know, maybe perhaps like the
accuracy of a psych maybe. Two to three specific
basis poi
nts different. And, you know, it's model selection process are really difficult. And, You know, meeting expectations
are really difficult, in different industries. And that's where
I'm kinda confused. What do you do in that instance? You know, you you've done
about everything you could. And, you know, no is not an acceptable answer. No. You're right. I honestly, there's not a
there's not a clean answer there. At least I haven't found it. I I know we're right at
the top. Of the hour. Michael, do
you have
to run to one meeting? Do you have time for one more? I don't. I can I can hang
for a couple of things? Okay. One more just
one more question. I see Abigail had a
question in the chat. I wanna make sure we get to. So, like, we like pipelines,
we like birth control, that stuff is good. We're engineering more. What's the thing where
you're like, nope, this is too much engineering. We do not need this. Oh, man. There's
examples of that. I think, okay. Yeah. I think some of our quants
can b
e a little guilty of trying to over engineer
their models and the implementation of their so I
work with a lot of quants that are brilliant people. They're smarter than I am. However, they there's a stereotype
that they are they're very siloed because they're very, very knowledgeable
about a single thing. Right? And they built this thing that
coded in c plus plus this is the best way to do this. And a lot of times, it it could be But when lined
up with everything else with a with a broader view,
it's not the best. There there's people that
are really more of your data engineers that have a
different perspective. And so there's a battle there all the
time between developer and implementer. And I do think that
a specific example, you know, the quants
want to develop a attribution, like, an automated c plus plus
attribution where if your data changes and the market changes, all these kind of it
It would be wonderful. They admitted that. There are a lot of things that
change in a bank day
over day that you're if you're trying
to automate all of that, One, I I would make the argument
do that in something that's more fluid like Python or
r so that in in in and manage that in GitHub so that
you can have multiple kind of a prod UAT development,
and you could go through a quick cycle, a day, a day
long cycle if you needed to, right, to change something. And also attribution usually
doesn't take any CPU. To do. So keep that loose. Don't over engineer that in c
plus plus and c sharp and
have this system, like, Don't
do it at the system level. Do it at the reporting level. So that's kinda I think to
answer your question clearly, I think it's more about where is the
best place to do this engineering. Right? That's an
important decision. Thank you. Yeah. No problem. Thank you so much, Michael, for taking the time to join
us today and thank you all for the great questions as well. See a lot of comments in the
chat right now about how much people enjoyed the
conversation today. So
Thank you. Oh, thank you. This was this was exciting, and I'm looking forward
to joining going forward. We'd love to have you come back. If people wanna connect with
you is LinkedIn the best place? Yeah. That's the easiest
place, to connect. Perfect. Yeah. Thank you. I just shared in the chat, but thank you all
so much for joining. Hope to see you
with us next week. Liz Siro from AT and T
is gonna be joining us. Have a great rest of the day.
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