Team Description

The Bank 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. Within the Agentic Claims area of this team, we are building solutions that drive down customer and contact center effort to correct payment issues while simultaneously resolving those challenges faster than ever. The team brings a variety of techniques to bear on these challenges, including agentic workflows, representation learning, and gradient boosting machines, to provide the intelligence that powers our real-time decision systems. By improving our claims resolution process we will also identify stronger leading indicators that the transaction fraud prevention side of the team to prevent more fraud and disputes.

Role Description

This role sits at the intersection of leadership and hands-on innovation, driving the transformation of claims management within the bank. You will lead cross-functional initiatives, balancing competing priorities across broad partnerships to deliver outsized impact. Success requires influencing stakeholders to adopt new techniques and expanding our solution set. While you will provide strategic oversight and lead a small team of data scientists to support the claims landscape, you will also remain deeply hands-onpersonally conducting data exploration, model development, and owning the end-to-end lifecycle of solution deployment. Beyond immediate claims management innovations, you will play a pivotal role in establishing the datasets, tools, and evaluation protocols that will serve as the foundation for broader Enterprise AI developments in this space.

In this role, you will:

• Partner with a cross-functional team of data scientists, software engineers, applied researchers, analysts, and product managers to deliver products customers love

• Leverage a broad stack of technologies LLMs, LangChain, Python, Ray, Spark, AWS, and more to operationalize 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

• Lead individual contributors by seeking to remove ambiguity and working closely with partners to shape the roadmap of the space

The Ideal Candidate is:

• 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.

• Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You27re not afraid to share a new idea.

• Technical. You27re 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.

• A data guru. 22Big data22 doesn27t 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 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 22STEM22 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 building agentic workflows

• At least 1 year of experience managing people

• At least 5 years27 experience in Python for large scale data analysis

• At least 5 years27 experience with machine learning

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 candidate27s 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.