Team Description

TheBank Customer Protection Debit & Claims Data Scienceteam builds the machine learning models that help our customersspend safely and get back on track if an issue does occur with their payments. We are constantly looking for ways to get ahead of fraudulent actors and scams before they have a negative impact on customers by analyzing historical transaction activity, account usage, merchant patterns and other data for signals that something is amiss. We use a variety of techniques, including representation learning and gradient boosting machines, to build purpose-built models that power our real-time decision systems and adapt quickly to emerging attack patterns. This role will bring these methodologies to bear on the debit authorization fraud side of our team - stopping debit fraud in real time as each transaction is authorized - spanning the full modeling spectrum, from proven techniques like gradient boosting to the frontier-AI approaches, such as graph and sequence learning, that are shaping the next generation of fraud detection.

Role Description

In this role, you will:

• Partner with a cross-functional team of data scientists, analysts, software engineers, and product managers to deliver a product that measurably keeps our customers safe from fraudulent activities.

• Leverage a broad stack of technologies 1 Python, Conda, AWS, H2O, Spark, SQL and more 1 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:

• Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day its about making the right decision for our customers.

• Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. Youre not afraid to share a new idea.

• Statistically-minded. Youve 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 doesnt 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 Bachelors 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 Masters 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:

• Masters Degree in STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in STEM field (Science, Technology, Engineering, or Mathematics)

• At least 3 years experience with machine learning for predictive tasks, especially classification on large, highly imbalanced datasets (fraud, risk, or anomaly detection)

• At least 3 years experience in Python and SQL. Preferred: production-quality, tested Python (pytest, mypy, linting, CI/pre-commit) and experience processing large-scale data with Spark (Polars, Snowflake/Snowpark)

• Experience building and tuning gradient boosting models (XGBoost, LightGBM, or H2O) and deploying them into real-time or production decision systems

• Experience building automated modeling pipelines  orchestrating training, evaluation, and deployment as reproducible workflows with Kubeflow Pipelines (KFP) on Kubernetes (or comparable pipeline/MLOps tooling)

• Experience with model backtesting, validation, and performance measurement - precision/recall and capture rates at low decline/alert volumes

• Experience coordinating data science projects in cross-functional teams

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.

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidates offer letter.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at theCapital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.