Behind Every Bond, There’s a Data-Minded Q

Data scientists are essential support for their companies. In turn, these Chicago companies are supporting them with proper tools, intellectual challenge and a clear mission.

Written by Anderson Chen
Published on Jul. 25, 2022
Behind Every Bond, There’s a Data-Minded Q
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There’s no 007 without Q, no modern-day spy aesthetic without the tech genius. The Quartermaster — James Bond’s primary support and played by nearly as many actors as there were for the triple-digit spy — has been a cultural mainstay in the film franchise. Though not as flashy or culturally ubiquitous as the eponymous poster boy, Q is nonetheless one of the central pillars of the films’ suave image, complete with the alphanumeric designation and high-tech gadgets. Like 007, Q is a title — passed down with prestige and pizzazz. 

For tech companies, the data scientist moniker also bears high standards. They carry similar duties in implementing tactical support and creating the operational dossier for their public-facing colleagues. Whatever mission parameters there may be, from wherever the problems may stem, data scientists will tackle the unknown armed with tech tools and statistics. 

With most companies and products shaped by data in the digital age, the work produced by data scientists power strategic initiatives, from sales and marketing all the way to the C-suite. While leadership touts creative vision and customer success managers upsell ambitious roadmaps, it is the statistical undercurrent of the data science team that built the pulpit from which they sermonize, a $95 billion analytics platform that is projected to become a $329 billion stage in 2026, according to Globe Newswire. 

But it’s not just about the money. At companies like digital consulting firm Inspire11 and fintech provider Avant, challenges present inroads where data scientists can elevate their craft and refine their processes. Every problem becomes an opportunity to explore limits and push past the cutting edge, for the sake of stakeholders and customers, but for their own intellectual pursuits as well. “New products provide new fields of study for the team, and I appreciate the chance to break out some different techniques,” said Avant’s Manager of Data Science Nils Carlsson. 

By virtue of the medium, data science is now an invaluable department in any tech company. The high-tech, backroom support of the team empowers both people and product, though their impact might not translate directly into the public spotlight. To highlight some of these talented Qs of their organizations, Built In Chicago sat down with three companies whose data scientists thrive in the data-forward culture. 

 

Avant office
Avant

 

Nils Carlsson
Manager, Data Science • Avant

 

Avant is a fintech company that offers banking services and products. The company’s digital platform includes mobile banking as well as access to its credit card and personal loan service. As they are responsible for the financial tools that customers use, employees at Avant take data analysis seriously. Data Science Manager Nils Carlsson said that the team’s responsibilities scale alongside the company’s growth. “We are finding solutions we didn’t even know we needed a few years ago,” he said. 

 

What is the coolest project you’ve worked on or are working on as a data scientist at your company? Why does this project stand out to you in particular?

The coolest project I’ve worked on at Avant was building our next generation credit model for the credit card product. It was a mammoth project that involved significant ETL just to get a dataset suitable for modeling, which we then optimized using our proprietary machine learning technology. A lot of data science work now relies on importing libraries that someone else has written, but building our own innovative algorithm from scratch was a great learning experience for our team. And given the importance of this model to the underwriting strategy, there was a lot of collaboration across the business to ensure that the model met everyone’s needs.  

Another part of what made this project so exciting was seeing the results — using this new model as part of the underwriting strategy, the AvantCard was named the third fastest growing Mastercard or Visa credit card in the country last year, and earlier this year we reached the one million total credit card customers milestone. I think it’s rare to be able to see the impact that your work is having on our customers so directly as a data scientist, which is something I’m very appreciative of at the company.

 

More generally, what excites you about the work you’re doing, the technology you’re using or the problems you’re working to solve?

As Avant grows, the responsibilities of the data science team do too. Avant now offers access to four products — loan, credit card, auto-refi and deposits — which has led to a completely new way of analyzing data. Our work has typically revolved around binary classification models using supervised learning.

I’m also excited by our work with distributed computing, particularly Spark. Our data used to fit into memory and we could manipulate it using Pandas, but as our customer base grows, that solution is not scalable. Spark offers a great Python API, Pyspark, that we are using as we scale up from single machine, in-memory computation. A lot of our work with Pyspark so far has been creating generic data manipulation pipelines. It’s amazing what we can do when we aren’t restricted to a single machine.

I think it’s rare to be able to see the impact that your work is having on our customers so directly.”

 

When do you love most about the culture of your team or the company as a whole? 

Our team has a very hands-on approach to the data science workflow — each data scientist manages all stages of their projects. When an Avant data scientist writes a SQL query to pull raw data, they perform some preparation and then feed the data into our machine learning framework to build the model. After the model is built, they validate it and write a report to describe the entirety of their work. This leads to a sense of ownership and satisfaction when projects are completed, since they know that everything that went into that project came from them. Two of our company values are initiative and problem-solving, and this process really reflects those.

I also want to mention our focus on automation — if it can be automated, in most cases it should. For example, I recently created a framework for batch scoring our models that presents a consistent API across all our sources of data so our models can be scored on any of our datasets with a click, no coding required. Our goal as a company is to help our customers, and this gives us more time to focus on how we can serve their needs. When we’re all encouraged to put the customer first, we can make a huge impact.

 

 

Jett Robinson
Principal, Data Science • Inspire11

 

Inspire11 is a digital management consulting firm that focuses on full-service implementation of client strategy and initiatives through digital tech and data analytics. The company’s talented consultants solve problems for different types of businesses across a variety of industries. For Jett Robinson, Inspire11’s principal data scientist, being able to learn novel concepts on the job is part of the appeal. “I was drawn to data science because I love learning about new things,” he said. 

 

What is the coolest project you’ve worked on or are working on as a data scientist at your company? Why does this project stand out to you in particular?

It might sound boring, but the most exciting project I worked on was forecasting prices for a group of thinly-traded commodities and associated freight costs. It was exciting because I was able to learn about a completely new domain while creating an end-to-end solution that users were able to use to make purchasing decisions. The revenue impacts were substantial and immediate, saving the client $15 million in the first year.

 

More generally, what excites you about the work you’re doing, the technology you’re using or the problems you’re working to solve?

For me, it is all about solving the problem. Every new problem I get to tackle gives me the opportunity to grab a new data set and learn from a new group of subject matter experts.

I love that our team has a no-stupid-questions ethos.”

 

When do you love most about the culture of your team or the company as a whole? Give an example of what this looks like in action.

I love that our team has a no-stupid-questions ethos. Data science is such a broad field, and we work in a variety of different industries and business contexts. No one person can be on top of all of it. When I don’t know something, I have no hang up about asking the team for help. They are always happy to provide judgment-free advice. Recently, I had a client ask how they should be thinking about Covid-era data. I had a couple of different ideas, but I asked the team and they gave me a lot of great suggestions.

 

 

Apexon group photo
Apexon

 

Madhumitha Vijayakrishna
Data Engineer • Apexon

 

Apexon digital-first technology services firm specializing in accelerating business transformation and delivering human-centric digital experiences. The safety of client data is also an important priority for Apexon’s employees, which is why Data Engineer Madhumitha Vijayakrishna is excited about the tech tools available to her. “The better the technology, the safer the data,” she said. 

 

What is the coolest project you’ve worked on or are working on as a data scientist at your company? Why does this project stand out to you in particular?

The scope of the project thrilled me as it was a green field project, which first required a working proof of concept (POC). This POC, which I am currently involved in as part of the data science team, will show a complete customer journey profile consisting of level details and their activity path based on the channels they use — mobile, Internet and more. The POC also incorporates a chart to visualize various segments based on customer attributes. This is an exciting opportunity for me to learn as it starts from scratch in terms of analysis and progresses gradually towards data modeling.

 

More generally, what excites you about the work you’re doing, the technology you’re using or the problems you’re working to solve?

There is a variety of technology stack used to solve problems, which makes it exciting as it is not restricted to just one tool. I’m inclined towards a career working with technology, as I’m focused on helping businesses keep their data safe. 

Transparency is key, and I can certainly see that within the team and how it leads to success.”

 

When do you love most about the culture of your team or the company as a whole? Give an example of what this looks like in action.

There is not just one, but many distinct qualities that I enjoy about the culture of my team and the company. First, the team as a whole is clear about its goals. Transparency is key, and I can certainly see that within the team and how it leads to success. Helping and lifting others is a constant. Another quality that stands out is the company as a whole focuses not just on what’s going well but also on what’s not, and seeing value from discussing those points. 

 

 

Responses have been edited for length and clarity. Images via listed companies and Shutterstock.

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