Principal Data Scientist, Credit Risk Management

Principal Data Scientist, Credit Risk Management

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 the relational database, 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 the big opportunities that help everyday people save money, time, and agony in their financial lives.

Team Description

Have you ever seen the headline in the news “Banks Pass Federal Reserve Stress Tests” and wondered how Capital One determines how much savings (or “capital”) it needs? Or maybe how we analyze the potential impact of the next recession? At the heart of these questions are sophisticated machine learning and 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 Credit Risk Management Modeling and Data Team, we blend cutting-edge quantitative methods with a deep understanding of our business, data, and regulatory environment to build predictive models for losses, account volumes, and outstanding balances. These models drive key strategic decisions for allowance setting, stress testing, and capital allocation as well as informing our earnings calls and recession preparedness.

If this sounds interesting to you, join us! As a Data Scientist on our team you’ll be at the forefront helping us to usher in the next wave of disruption by using the latest technology to build & deploy machine learning models that leverage new data sources to provide powerful insights about our portfolio and growth opportunities. 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. You will do it all in a collaborative environment that values your insight, encourages you to take on new responsibilities, promotes continuous learning, and rewards innovation.

Role Description

In this role, you will:

• Partner with a cross-functional team of analysts, data scientists, software engineers, and product managers to deliver a product customers love

• Leverage a broad stack of technologies — AWS, Python, Dask, Spark, and more — to reveal the insights hidden within huge volumes of data

• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation

• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The Ideal Candidate is:

• Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.

• Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.

• Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.

• A data guru. “Big data” doesn’t phase you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science.

Basic Qualifications:

• Bachelor’s Degree plus 5 years of experience in data analytics, or Master’s Degree plus 3 years in data analytics, or PhD

• At least 1 year of experience in open source programming languages for large scale data analysis

• At least 1 year of experience with machine learning

• At least 1 year of experience with relational databases

Preferred Qualifications:

• Master’s Degree 3+ years of experience or PhD in quantitative field (Economics, Statistics, Computer Science, Applied Math, Engineering)

• Experience with data science within a work environment

• At least 1 year of experience working with AWS

• At least 3 years of experience leveraging the PyData ecosystem (pandas, numpy, scikit-learn, dask)

• At least 3 years of experience with econometrics and machine learning

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.