Manager, Data Scientist

Capital One • McLean, VA

1d • Onsite

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.

Team Description
In Capital One’s Model Risk Office, we defend the company against model failures and find new ways of making better decisions with models. We use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise. We understand that we can’t prepare for tomorrow by focusing on today, so we invest in the future: investing in new skills, building better tools, and maintaining a network of trusted partners. We learn from past mistakes, and develop increasingly powerful techniques to avoid their repetition.

Role Description-
In this role, you will:
Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
Leverage a broad stack of technologies — Python, Conda, AWS, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
Build machine learning models to challenge “champion models” that are deployed in production today
Contribute to the model governance framework for the next generation of machine learning models
Flex your interpersonal skills to present how model risks could impact the business to executives
Validate a wide variety of models across multiple business domains within our Enterprise Services devision

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
• At least 4 years’ experience with SQL
• At least 4 years’ experience building or validating models related to fraud detection, digital marketing, cybersecurity, or sensitive data detection.

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
• Leverage a broad stack of technologies — Python, Conda, AWS, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Build machine learning models to challenge “champion models” that are deployed in production today
• Contribute to the model governance framework for the next generation of machine learning models
• Flex your interpersonal skills to present how model risks could impact the business to executives
• Validate a wide variety of models across multiple business domains within our Enterprise Services devision

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Manager
• Number of Employees: 5,001-10,000 employees