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, Reinforcement Learning, and Transformer-based architectures for a large-scale fintech company. The role involves partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building ML models through all phases of development.

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
  • Master's Degree in a quantitative field or MBA with quantitative concentration or equivalent experience
  • PhD in a quantitative field or equivalent experience
  • 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
  • building, deploying, and maintaining high-scale, production-grade ML systems using MLOps practices
  • AWS
  • Kubeflow
  • CI/CD pipelines
  • developing and optimizing state-of-the-art Deep Learning models
  • Transformer-based architectures
  • PyTorch
  • distributed training with multi-GPU optimization
  • high-performance, distributed data processing for petabyte-scale feature engineering
  • DASK
  • PySpark

What the JD emphasized

  • production-grade ML systems
  • Transformer-based architectures
  • distributed training with multi-GPU optimization
  • high-performance, distributed data processing for petabyte-scale feature engineering

Other signals

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