Manager, Data Scientist - Recommendation & Personalization Systems

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

Manager, Data Scientist role focused on building and deploying personalized recommendation engines using Foundation Models and Reinforcement Learning. The role involves partnering with cross-functional teams, leveraging technologies like Python and AWS, and building ML models through all phases of development. Expertise in Transformer-based architectures and scalable systems is required.

What you'd actually do

  1. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
  2. Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
  3. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  4. Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Required

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

Nice to have

  • PhD in STEM field
  • 3+ years of experience in data analytics
  • 3+ years of hands-on experience building, deploying, and maintaining high-scale, production-grade ML systems using MLOps practices, including AWS, Kubeflow, and CI/CD pipelines
  • Deep expertise (4+ years) in developing and optimizing state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch and distributed training with multi-GPU optimization
  • Extensive experience (4+ years) with high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark

What the JD emphasized

  • high-scale ML models
  • cutting-edge personalized recommendation engines
  • original research into homegrown Foundation Models
  • advanced Reinforcement Learning techniques
  • state-of-the-art scalable architecture
  • Transformer-based architectures
  • sophisticated Recommender Systems
  • production-grade ML systems
  • Deep expertise (4+ years) in developing and optimizing state-of-the-art Deep Learning models, specifically Transformer-based architectures, using PyTorch and distributed training with multi-GPU optimization
  • Extensive experience (4+ years) with high-performance, distributed data processing for petabyte-scale feature engineering using frameworks like DASK and PySpark

Other signals

  • recommendation engines
  • Foundation Models
  • Reinforcement Learning
  • Transformer-based architectures
  • Recommender Systems