Principal Data Scientist

Locations: VA - McLean, United States of America, McLean, Virginia

Principal Data Scientist

Principal Data Scientist - Consumer Credit Risk Management Models and Data

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and cutting-edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock opportunities that help everyday people save money, time, and agony in their financial lives.

Team Description

The Card Loss Forecasting and Allowance team uses machine learning models to forecast and optimize future losses associated with Capital One's credit card portfolio. We work with the enterprise tech teams to use the latest technologies and algorithms to build predictive models and automate insight generation.

As a Data Scientist, you will focus on loss forecasting modernization as we continuously enhance the platform that executes the card loss forecasting process that feeds earnings, financial planning, and stress testing.

At the heart of these questions are sophisticated econometric loss models that help us understand the ways in which the economy impacts our loan portfolios and guide strategic decision making at the highest levels of Capital One.

In the Consumer Credit Risk Management Models and Data Team, we blend cutting-edge quantitative methods with a deep understanding of our business, data, and regulatory environment to build and deploy predictive models for losses, account volumes, and outstanding balances. These models drive key strategic decisions for loss allowances, stress testing, and capital allocation.

If this sounds interesting to you, join us! As a Data Scientist on the deployment & platform side of the team, you'll be at the forefront helping us to usher in the next wave of disruption by using the latest technology to deploy, optimize and modernize model pipelines and execution platforms that enable machine learning models to provide powerful insights about our portfolio and growth opportunities through new data sources. You will partner with best-in-class data scientists, analysts, and engineers to innovate solutions that directly impact the company's bottom line in a meaningful way.

Role Description

In this role, you will:

1. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love.
2. Leverage a broad stack of technologies - Python, Conda, AWS, Spark, and more - to reveal the insights hidden within huge volumes of data.
3. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
4. Translate the complexity of your work into tangible business goals.

The Ideal Candidate is:

1. Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing for our customers.
2. A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo.
3. Technical. You're comfortable with open-source languages and have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
4. A data guru. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures.
5. Statistically-minded. You've built models and know how to interpret a confusion matrix or a ROC curve.

Basic Qualifications:

1. Currently has, or is in the process of obtaining a Bachelor's Degree plus 5 years of experience in data analytics, or currently has a Master's Degree plus 3 years in data analytics, or currently has a PhD.
2. At least 1 year of experience in open source programming languages for large scale data analysis.
3. At least 1 year of experience with machine learning.
4. At least 1 year of experience with relational databases.

Preferred Qualifications:

1. Master's Degree in