Senior Manager, Data Science - 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. The successful candidate will join the Card Fraud Model Risk team, which is tasked with evaluating risk for Card Fraud models used throughout the customer lifecycle. This role covers fraud detection at various stages, including application, transaction, and payment, for both first‑party and third‑party use cases. The team conducts thorough assessments of existing fraud models and builds independent challenger models to ensure effective oversight. Working within a small, innovative group, you will act as a key individual contributor investigating advanced algorithms such as graph, neural, tree‑based, and sequence methods to create robust challenger models. Additionally, you will provide mentorship to junior data scientists as you explore cutting‑edge solutions and oversee card fraud model risk. 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 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. 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. 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. 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 7 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 5 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 2 years of experience performing data analytics At least 2 years of experience leveraging open‑source programming languages for large‑scale data analysis At least 2 years of experience working with machine learning At least 2 years of experience utilizing relational databases Preferred Qualifications PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics At least 1 year of experience working with AWS At least 1 year of experience managing people At least 5 years’ experience in Python, Scala, or R for large‑scale data analysis At least 5 years’ experience with hands‑on Machine Learning model development and deployment Experience with model development/deployment pipeline (e.g. Kubeflow, Kubernetes) is preferred. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. Salary McLean, VA: $229,900 - $262,400 for Sr Mgr, Data Science New York, NY: $250,800 - $286,200 for Sr Mgr, Data Science Richmond, VA: $209,000 - $238,500 for Sr Mgr, Data Science Riverwoods, IL: $209,000 - $238,500 for Sr Mgr, Data Science 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. Benefits 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. Equal Opportunity 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 for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23‑A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901‑4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. #J-18808-Ljbffr Capital One