Data Science Manager, US Card

Center 1 (19052), United States of America, McLean, Virginia

Data Science Manager, US Card

Manager, Data Scientist, US Card

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-

As part of the US Card data science organization, this team focuses on the customer management business, and builds top tier machine learning solutions to meet the various needs primarily within customer behavioral prediction and valuation forecasts. We work in a team environment, collaborating closely with cross-functioning teams including the Upmarket business, engineers, analysts, data science/quants, products.

Role Description-

This role will be focusing on building the next generation of customer management valuation models. 

Key responsibilities includes:

• Apply, develop and deliver advanced machine learning solutions solving the real business problem in Upmarket Customer Management valuation

• Develop, maintain and leverage model training and inference pipelines using enterprise level tools and platforms with high code quality

• Connect/translate data science solutions with/into business outcomes and communicate with senior stakeholders

• Well manage existing model suites and adopt the best model developer ownership practices interacting with Capital One's 2nd/3rd line defenses

The Ideal Candidate profile:

• Strong software engineering skills and the ability to develop end to end model pipelines in Python

• Proficiency with various machine learning algorithms and predictive solutions

• Efficiency in communicating technical details to various of audiences

• Ability to quickly understand the immediate business needs

Basic Qualifications:

• Currently has, or is in the process of obtaining a Bachelor’s Degree plus 6 years of experience in data analytics, or currently has, or is in the process of obtaining a Master’s Degree plus 4 years of experience in data analytics, or currently has, or is in the process of obtaining PhD plus 1 year of experience in data analytics, with an expectation that required degree will be obtained on or before the scheduled start date

• 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, 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.

New York City (Hybrid On-Site): $201,400 - $229,900 for Mgr, Data Science

Candidates hired to work in other locations will be subject to the pay range associated with that location, and