Manager, Data Science - Emerging ML

Capital One Capital One · Banking · McLean, VA +2

Manager, Data Science - Emerging ML role at Capital One focuses on research and development of new AI technologies, specifically Embeddings and Foundation Models. The role involves conducting research into self-supervised learning and transformer models, building customer behavioral models, and refining integration patterns for encoder and decoder models. It requires building ML models from design through training, evaluation, and validation, and partnering with engineering for production deployment serving over 50 million customers. The candidate will also conduct experiments to guide improvements in marketing, servicing, and fraud prevention, and write software for data analysis using Spark and AWS.

What you'd actually do

  1. Build machine learning models through all phases of development, from design through training, evaluation and validation, and partner with engineering teams to operationalize them in scalable and resilient production systems that serve 50+ million customers.
  2. Partner closely with a variety of business and product teams across Capital One to conduct the experiments that guide improvements to customer experiences and business outcomes in domains like marketing, servicing and fraud prevention.
  3. Write software (Python, Scala, e.g.) to collect, explore, visualize and analyze numerical and textual data (billions of customer transactions, clicks, payments, etc.) using tools like Spark and AWS.

Skills

Required

  • Bachelor's Degree in a quantitative field plus 6 years of experience performing data analytics
  • Master's Degree in a quantitative field or an MBA with a quantitative concentration plus 4 years of experience performing data analytics
  • PhD in a quantitative field plus 1 year of experience performing data analytics
  • leveraging open source programming languages for large scale data analysis
  • working with machine learning
  • utilizing relational databases

Nice to have

  • PhD in “STEM” field
  • Experience working with AWS
  • 4 years’ experience in Python, Scala, or R
  • 4 years’ experience with machine learning
  • 4 years’ experience with SQL

What the JD emphasized

  • petabytes of data

Other signals

  • foundation models
  • embeddings
  • transformer models
  • self supervised learning
  • customer behavioral models
  • encoder and decoder models