Manager, Data Scientist - Privacy-preserving, Machine Learning and Analytics

Manager, Data Scientist - Privacy-preserving, Machine Learning and Analytics

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

The privacy-preserving machine learning and analytics team is an innovation-focused team dedicated to the research, development, and production deployment of Privacy-Enhancing Technologies (PETs). We specialize in cutting-edge techniques like Differential Privacy (DP) and Synthetic Data Generation to transform how sensitive data is used across Capital One. Our work provides foundational capabilities that ensure rigorous privacy protection while empowering data scientists and engineers to drive responsible innovation, solve complex business challenges, and unlock new data utility at scale. 

Role Description

In this role, you will:

• Lead the full lifecycle of anonymization projects, overseeing the development and scaling of differentially private data analysis pipelines and sophisticated synthetic data generators for complex, high-dimensional data (e.g., financial transactions, text, and tabular datasets).

• Conduct independent, hands-on research into the state-of-the-art in differential privacy, secure computation, and privacy-preserving machine learning.

• Evaluate, adapt, and implement advanced algorithmic approaches to optimize for data utility and privacy guarantees in production environments.

• Design, build, and deploy production-ready anonymization pipelines, leveraging a broad stack of technologies within the cloud environment.

• Establish and manage rigorous evaluation frameworks to quantify the fidelity, utility, and privacy guarantees of generated and anonymized data.

• Partner with cross-functional teams of data scientists, software engineers, product managers, and governance specialists to ensure the seamless integration and adoption of new privacy capabilities across the enterprise.

• Represent the team in technical discussions with external academic partners, focusing on joint development to accelerate internal capability building.

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.

• A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.

Basic Qualifications:

• 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