Manager, Data Scientist- Capital One Data Insights and Analytics

Capital One • McLean, NY

7d • Hybrid

About The Position

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. The Commercial Business Analysis (CBA) team partners with senior business leaders across Capital One to deliver on our vision to become the top middle market bank. As part of the team’s scope, we have a Capital One Data Insights and Analytics (CODIA) team dedicated to innovating with new sources of data to drive long-term value for our clients and the bank. We are currently expanding to integrate advanced machine learning models, modernize our data infrastructure, and identify new delivery models.

Requirements

• Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics
• A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics
• A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics
• At least 1 year of experience leveraging open source programming languages for large scale data analysis
• At least 1 year of experience working with machine learning
• At least 1 year of experience utilizing relational databases

Nice To Haves

• PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
• At least 1 year of experience working with AWS
• At least 4 years’ experience in Python, Scala, or R for large scale data analysis
• At least 4 years’ experience with machine learning and embeddings
• At least 4 years’ experience with SQL
• Experience working with consumer and/or commercial banking data

Responsibilities

• Partner with a cross-functional team of data scientists, business analysts, and relationship managers to deliver a product that creates real value for our clients.
• Partner with enterprise AI teams to build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Conduct rigorous statistical analysis and back-testing against third-party data to integrate new datasets and mathematically validate insights.
• Modernize data infrastructure by developing tailored analytical tools on core enterprise data schemas and implementing real-time automated validation controls.
• Support long-term technical integrations for complex business projects, including comprehensive run-off analysis, underwriting automation, and advanced risk and portfolio management.

Benefits

• comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being
• performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

What This Job Offers

• Job Type: Full-time
• Career Level: Manager