Manager, Data Scientist - Card Mainstreet Acquisitions

About this role

Manager, Data Scientist - Card Mainstreet Acquisitions

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: The Mainstreet Acquisitions team sits at the forefront of technology, model development, and credit strategy innovation at Capital One and is continuously working to innovate ways we handle data, build models, and perform analysis. Positioned at the intersection of model architecture, risk strategy, and emerging AI capabilities, this role serves as the key technical and analytical driver for our multi-model decisioning pipelines and valuations frameworks. We use the latest techniques in machine learning to build predictive models and utilize the advantages of large-scale data and cloud-based processing. Through these techniques, we extract relevant insights from data to tackle a huge variety of business problems, and we partner with credit programs to leverage our insights to improve their customer-facing policies.

In this role, you will: • Identify, analyze, and mitigate systemic risks within a newly launched, multi-model business decisioning infrastructure through integrated monitoring frameworks

• Identify gaps and vulnerabilities using advanced statistical tools and techniques, leveraging pre-existing infrastructures or building from scratch where necessary

• Build and deploy solutions centered on Large Language Models (LLMs) and Agentic AI

• Partner with cross-functional teams of data scientists, business analysts, and software engineers to deliver on products and tools for the business

• 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 it’s about making the right decision for our customers.

• 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.

• 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.

• 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.

• 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 6 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 4 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 1 year of experience performing data analytics

• At least 1 year of experience leveraging open source programming languages for large scale data analysis

• At least 1 year of experience working with machine learning

• At least 1 year of experience utilizing relational databases

Preferred Qualifications: • PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics

• At least 1 year of experience working with AWS

• At least 4 years’ experience in Python, Scala, or R for large scale data analysis

• At least 4 years’ experience with machine learning

• At least 4 years’ experience with SQL

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: $197,300 - $225,100 for Mgr, Data Science   New York, NY: $215,200 - $245,600 for Mgr, Data Science

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