Principal Data Scientist

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

Principal Data Scientist

Principal Associate, Data Scientist, Corporate Strategy - IDEAS 

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 100 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

The Corporate Strategy team advises our company’s CEO and senior leadership on the most critical decisions facing the company across financial services, technology, and beyond. We engage in a discrete number of high impact issues that drive the performance and direction of the company. Projects focus primarily on formulating enduring and profitable growth and value enhancement strategies and then working with our business and staff teams to ensure the maximum impact.

The Integrated Data Engineering, Analysis, and Science—IDEAS—team within Corporate Strategy, creates insights on consumer behavior, market conditions, and peer performance by analyzing and applying statistical modeling and machine learning to a wealth of customer-level and market-level data. In this high visibility role, you will work on a diverse set of strategic projects based on the top of house priorities leveraging your data science skills. 

Role Description

In this role, you will:

• Partner with a cross-functional team of data scientists, software engineers, and data analysts, data engineers, Machine Learning engineers, and business partners to deliver actionable insights that drive Capital One's strategies.

• Leverage a broad stack of technologies — Python, R, Jupyter, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual 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:

• 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.

• 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.

• 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.

• Statistically-minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.

• A data guru. “Big data” doesn’t faze 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:

• Currently has, or is in the process of obtaining a Bachelor’s Degree plus 2 years of experience in data analytics, or currently has, or is in the process of obtaining Master’s Degree, or currently has, or is in the process of obtaining PhD, with an expectation that required degree will be obtained on or before the scheduled start date

• 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