Principal Quantitative Analyst - People Strategy & Analytics

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

Principal Quantitative Analyst - People Strategy & Analytics

At Capital One 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 Quantitative Analyst 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 cloud 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.

People Strategy & Analytics is an emerging field where Capital One is on the leading front. Our team brings data, analytics, and insights to shape critical talent decisions and strategy at Capital One. We work closely with HR partners and senior executives in shaping talent policy, automating real-time data and improving talent decision-making. The team is composed of people with diverse skills and backgrounds including: data analysts and engineers, product managers, data scientists, consultants and strategists, business analysts, HR specialists, economists, and Industrial Organizational Psychologists.
 

Responsibilities and Skills:
- Partner with cross-functional teams from Legal and Human Resources to assess Capital One's compensation system.

- Utilize Python, SQL, and Quicksight to build modular, scalable, statistical models and reporting infrastructure and dashboards that will provide key insights about pay equity & fairness.

- Provide insights that drive decisions and help shape policy impacting 50,000+ associates.

- Flex your interpersonal skills to translate the complexity of statistical model results into tangible insights and actionable recommendations.

- Apply root cause analysis, such as causal inference, to answer why an observed phenomenon happens and suggest actions that could effectively address concerns.

Expertise in quantitative analysis is central to our success in all markets. Our modelers thrive in a culture of mutual respect, excellence and innovation.

Successful candidates would possess:
- Strong understanding of quantitative analysis methods in relation to financial institutions.
- Demonstrated track-record in machine learning and econometric analysis.
- Experience utilizing model estimation tools.
- Ability to clearly communicate modeling results to a wide range of audiences.
- Drive to develop and maintain high quality and transparent model documentation.
- Strong written and verbal communication skills.

- Strong presentation skills.
- Ability to fully own the model development process: from conceptualization through data exploration, model selection, validation, deployment, business user training, and monitoring.

 - Proficiency in key econometric and statistical techniques (such as predictive modeling, logistic regression, survival analysis, panel data models, design of experiments, decision trees, machine learning methods.)

Basic Qualifications:

 - Currently has, or is in the process of obtaining a Bachelor’s Degree plus at least 5 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus at least 3 years in data analytics, 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 2 years of programming experience or financial modeling or econometric modeling (can include Graduate School Research work.)

Preferred Qualifications:

- Master’s Degree or PhD in Statistics, Economics, Mathematics, Operations Research, Engineering, Physics or related discipline.

- At least 3 years of experience in statistical techniques such as regression, root cause analysis, causal inference, classification and clustering.

- At least 3 years of experience with developing and implementing models using modern scripting languages (Python, R or other statistical languages.) 

- At least 3 years of experience working with large-scale, cloud-based coding environments and databases (Python, SQ