Principal Associate, Data Scientist - Model Risk Office

Principal Associate, Data Scientist - Model Risk Office 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. The successful candidate will specifically support the Card Fraud Model Risk team, which is responsible for evaluating the model risk associated with the Card Fraud models utilized across the entire customer lifecycle. This includes models deployed during the application, transaction, and payment stages, addressing both first‑party and third‑party fraud use cases. The team executes effective challenges through the rigorous assessment of developed fraud models and the construction of independent challenger models. 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 the 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. Flex your interpersonal skills to translate the complexity of your work into tangible business goals. The Ideal Candidate is: 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. Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing data science solutions using open‑source tools and cloud computing platforms. Statistically‑minded. You’ve built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning. A data guru. “Big data” doesn’t faze you. You have the skills to retrieve, combine, and analyze data from a variety of sources and structures. You know understanding the data is often the key to great data science. 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 5 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 3 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). Preferred Qualifications: Master’s Degree in a STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in a STEM field. At least 3 years’ experience in Python, Scala or R. 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 other cloud computing platform. Experience with model development/deployment pipelines (e.g. Kubeflow). Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full‑time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part‑time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $161,800 - $184,600 Riverwoods, IL: $147,100 - $167,900 This role is also eligible to earn performance‑based incentive compensation, which may include cash bonus(es) and/or long‑term incentives (LTI). Incentives could be discretionary or non‑discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries. #J-18808-Ljbffr Capital One National Association