Overview
Data is at the center of everything we do. As a startup we disrupted the credit card industry by personalizing every credit card offer using statistical modeling and relational databases, and over time we have grown into a Fortune 200 company and a leader in data‑driven decision‑making.
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 statistics, software engineering, and business expertise to drive the best outcomes in Risk Management and across the enterprise.
Role Description
In this role you will:
- Partner with a cross‑functional team of data scientists, software engineers, and product managers to deliver a product customers love
- Leverage a broad stack of technologies – Python, Conda, AWS, H2O, Spark, and more – to reveal 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
- Translate the complexity of your work into tangible business goals using your interpersonal skills
Ideal Candidate
- Creative – you thrive on bringing definition to big, undefined problems and love asking questions and pushing hard to find answers
- Technical – you are comfortable with open‑source languages, passionate about developing further, and have hands‑on experience building data science solutions on cloud platforms
- Statistically‑minded – you’ve built, validated, and backtested models, and understand how to interpret a confusion matrix, ROC curve, clustering, classification, sentiment analysis, time series, and deep learning
- A data guru – “big data” doesn’t faze you; you can retrieve, combine, and analyze data from a variety of sources and structures, knowing that understanding the data often leads to great outcomes
Basic Qualifications
- Currently has, or is in the process of obtaining one of the following with the expectation that the required degree will be obtained on or before the scheduled start date:
- Bachelor’s Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
- 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 3 years of experience performing data analytics
- PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)
Preferred Qualifications
- Master’s Degree in a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or a PhD in a STEM field
- At least 3 years’ experience in Python, Scala, or R and experience with PySpark
- At least 3 years’ experience with SQL
- At least 3 years’ experience with machine learning model development and deployment
- At least 1 year’s experience with AWS or another cloud computing platform
- Experience with model development/deployment pipelines (e.g., Kubeflow)
Equal Opportunity Statement
Capital One is an equal opportunity employer (EOE, including disability/veteran) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace and will consider qualified applicants with a criminal history in a manner consistent with applicable laws.
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