Data Scientists Share an Inside Look at Their Game-Changing Work

For these New York professionals, smart data is the name of the game.

Written by Conlan Carter
Published on Jun. 21, 2024
Data Scientists Share an Inside Look at Their Game-Changing Work
Brand Studio Logo

“I think data adds a story to tell. It doesn’t detract. It’s part of telling the whole story.”

For Eno Sarris, a baseball analytics writer for The Athletic, the importance of statistics in baseball — and in sports in general — is not separate from the experience of watching a game. If a baseball player hits the home run that clinches the game or makes a Hail Mary toss from the outfield that secures the third out, fans have a great story to tell the next day. But for dedicated fans, data provides context that fleshes out the fuller story. If that home run came from a rookie player with a low batting average, the story evolves into the start of an exciting professional baseball career — raising the stakes for lovers of baseball and numbers alike.

Data scientists working in tech play a similar role in fleshing out the story of a tech company, and, just like sports analytics shape the future of how games are planned, played and enjoyed, data has a direct hand in the future success of tech businesses. Some of the major applications of data science in businesses include predicting market trends, increasing security, improving internal finances and streamlining services, according to Harvard Business School.

In New York, the day of a data scientist encompasses all of these tasks and more, including collaborating and creating new forecasting models and new features that directly impact the customer experience. Built In NYC spoke with three data scientists to get an inside look at how they add their chapter to the greater story of a business.
 

Tim Mulligan
data science manager • Capital One

Capital One is a banking and financial services company that uses digital fluency to transform everything about the customer experience.

 

What does a typical workday look like for a Capital One data scientist?

Each day, I get to work with some wonderful business analysts, data scientists and software engineers to manage one of Capital One’s credit risk models. That model supports our business customers with a “no preset spending limit,” which means those cards have a flexible spending limit and can adapt to business needs. 

Our team manages this credit risk model with a robust stack of cloud-enabled development tools that support me in doing my work, including, but not limited to, AWS. Capital One has open-source software that makes data analysis and monitoring easy. In my day to day, I use the Python data science stack. I’m constantly blown away by the tech and the generosity and talent of people at Capital One.
 

Tell us about a project you’re working on right now. Is there anything you find rewarding or challenging about it?

My team helped to release a new version of a machine learning model that helps mitigate fraud and protect legitimate transactions. It’s a very large and tactical effort to get all the data we need to make decisions in real time. We owned this entire process from end to end. Our team developed the model, put it into production and trained others on its use, and we continue to govern it today. 

That credit risk machine learning model is unique at Capital One. The model makes personalized decisions in real time so every customer gets individualized treatment. As a business owner swipes their credit card, our system helps confirm that this is a genuine transaction. When I do my job, I know I’m assisting someone else’s business to run smoothly, gain rewards and feel protected from fraud.

 

What’s the culture like on the Capital One team? How do you enable team members to grow their knowledge and connect?

From a professional standpoint, we have a rich data science culture at Capital One. There are two internal conferences each year where Capital One data scientists come together and talk about the infrastructure we use, the awesome products we’re making and the new tech we’re excited about. Outside of these formal opportunities to connect, we have a culture of curiosity that extends to more informal moments between teammates and peers. Often, my team will talk about techniques and different ways we create data ecosystems or machine learning models and their improvements. 

On the personal front, I’m a dad and have a family that is my top priority. I’ve always felt Capital One values caregivers and those who need time outside of work. I rarely bring work with me. Overall, you get to learn, work on new tech and prioritize your personal life. Capital One is the best place to be a data scientist.

 

Capital One is the best place to be a data scientist.”

 

 

 

Shi Fan
Lead Data Scientist • Current

Current is a consumer fintech and payments platform on a mission to improve financial outcomes for its customers.

 

What does a typical workday look like for a Current data scientist?

As a data scientist, I help contribute to the success at Current in mainly two ways: working with business stakeholders to drive data-informed decisions and promoting best practices among my peers to increase the efficiency of our work.

An example project could start with questions like “If we want to roll out feature XYZ by testing on a select group of users, who are the ones we should go after?” “What metrics do we want to track?” “How long should we run the test for?” and “How should we interpret the results and make a decision based on those?” This involves collaborating with the business, product and engineering teams to understand and refine the scope based on priority and feasibility and owning the process of going from raw data to insights and recommendations.

Technology-wise, I use dbt and Looker for analytics and reporting, as well as Python, VertexAI, BQML and Colab Notebooks for ML and statistical modeling.


 

Tell us about a project you’re working on right now. Is there anything you find rewarding or challenging about it?

Right now, I am working on a forecasting project with a few business stakeholders. The main goal is to gain predictive insights into several of our top unit economic metrics. This project has an impact on helping us better understand the performance and trends of those levers, in addition to the way they interact with each other, to tie back to some of our financial goals.

What I find rewarding about this project is that it enables me to apply the modeling techniques in a business-oriented context and learn from my stakeholders through their way of thinking. The challenging part is that out-of-the-box tools are not always directly useful, so I need to triangulate them to fit the problem that I am solving. But that is also the fun part.

 

The challenging part is that out-of-the-box tools are not always directly useful. But it's also the fun part.”

 

What’s the culture like on the Current team? How do you enable team members to grow their knowledge and connect?

We value knowledge sharing, from weekly roundtables to monthly team meetings. Team members have regular opportunities to get their work peer-reviewed and gain thoughtful feedback from others. Some of those can potentially become frameworks for the team to reference or materials to demo more broadly. We encourage folks to be curious to learn and share their thoughts on the initiatives they are working on or ones that feel important to them. Socially, we have team outings every other month or so to get our folks connected through various team-bonding activities in addition to the bi-weekly company-wide happy hours.

 

 

Patrick Tan
Data Scientist • Click Therapeutics

Click Therapeutics delivers safe and effective software prescription medical treatments to patients in need.

 

What does a typical workday look like for a Click Therapeutics data scientist?

Being a data scientist at Click Therapeutics involves collaborating closely with machine learning engineers, data engineers and data analysts to execute multiple machine learning projects supporting external productions and internal use cases. For example, we design machine learning projects that help understand our users’ behaviors and elevate their user experience through using our products.

Extensive collaboration with other teams, such as software engineering and clinical development, is essential to provide data solutions that address product development, improve clinical research and development and generate business value. A typical day for a data scientist includes reviewing project updates, coordinating with the team to set agendas and priorities, performing data collection, cleaning, exploration, developing machine learning algorithms, attending relevant stakeholder meetings and presenting findings or writing reports. We also leverage various platforms and cloud services to scale our data architecture, manage algorithms and support the entire product development lifecycle.


 

Extensive collaboration with other teams is essential to provide data solutions.”

 

Tell us about a project you’re working on right now. Is there anything you find rewarding or challenging about it?

As a biotech company, Click Therapeutics develops and commercializes software as a prescription medical treatment for unmet medical needs. Our team of data scientists plays a crucial role by contributing to the development of machine learning algorithms.

In one of our products for schizophrenia, we contribute to building machine learning solutions that help investigate how to treat the negative symptoms. This initiative has the potential to eventually benefit the 24 million patients affected by serious mental illnesses, estimated at one in 300 people worldwide. Through this project, we strive to develop cutting-edge therapies that address unmet medical needs currently inadequately met by existing treatments. The project presents challenges as it involves developing innovative machine learning solutions for complex healthcare problems within a framework of regulatory requirements. However, it also provides an exciting opportunity for collaboration among talented and passionate teams within our organization.


 

What’s the culture like on the Click Therapeutics team? How do you enable team members to grow their knowledge and connect?

The data team strives to foster a culture that emphasizes data-driven decision-making throughout the organization. To maintain continuous learning and innovation, staying informed about the latest advancements in data science and healthcare technology is crucial. The company — along with the team — strongly supports continuous learning through various initiatives, including regular team meetings, a data science journal club, mentorship programs and brainstorming sessions that explore novel areas of interest in data science and healthcare.

Click Therapeutics offers benefits like a professional development stipend program, which encourages employees to enhance their skills and contribute more effectively to the organization —  fostering a culture of growth and collaboration. One of Click Therapeutics’ values is to make work fun, and we often organize company-wide events such as summer and winter bashes and support department-level or team-level events and off-sites. The company also offers colleague resource groups such as, the Women-at-Click CRG, the LGBTQ CRG and Click Cares CRG — all to promote employee engagement, support and workspace well-being.

 

 

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

Hiring Now
Click Therapeutics
Healthtech • Biotech • App development