Manager, Data Science and Optimization - Retail Bank

Manager, Data Science and Optimization - Retail Bank 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.

Description: Retail Bank is a high performing modeling and analytics team that is on a mission to define the next generation of banking. The Bank team has a relentless focus on the craft of statistical modeling and innovation with a target towards continually improving decision making and delivering value to the business. Using the latest in machine learning and distributed computing technologies, you will be building the next generation of data products to enable automation and aim for the right decision at the right time for in-moment decisioning.

Description In this role, you will: Formulate & Solve Complex Problems: Translate ambiguous business challenges into structured mathematical problems. Design and implement optimization models (linear, mixed-integer, non-linear, and heuristic) to improve decision-making. Build & Deploy Scalable Models: Develop, test, and deploy production-grade optimization algorithms and simulation models using Python and commercial/open-source solvers. Collaborate Cross-Functionally: Partner closely with Product, Engineering, and Business teams to integrate optimization engines into existing software systems and workflows. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation The Ideal Candidate is: Strategic Impact & Experimental Rigor. Proven track record of driving strategic business value by optimizing customer experience funnels and risk policies, effectively evaluating external data, and institutionalizing closed-loop experimental frameworks to align predictive backtesting with live operational outcomes. 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. 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.

Bachelor’s Degree plus 6 years of experience in data analytics, or Master’s Degree plus 4 years of experience in data analytics, or PhD plus 1 year of experience in data analytics At least 2 years’ experience in open source programming languages for large scale data analysis At least 2 years’ experience with machine learning At least 2 years’ experience with relational databases Preferred Qualifications: PhD in “STEM” field (Science, Engineering, Operations Research or Mathematics) plus 2 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 Experience in using numerical optimization to solve business problems Experience with linear, non-linear, integer programming