Manager, Data Scientist - Credit Review

Capital One Capital One · Banking · McLean, VA +4

Manager, Data Scientist role at Capital One focused on challenging existing production models with new statistical and machine learning models. The role involves leveraging technologies like Python, AWS, H2O, and Spark to analyze large volumes of data, build and validate models, and collaborate with cross-functional teams. The ideal candidate has a strong statistical background, experience with various machine learning techniques, and a passion for innovation.

What you'd actually do

  1. Leverage a broad stack of technologies, such as, Python, Conda, AWS, H2O, Spark, and more, to reveal the insights hidden within huge volumes of numeric and textual data
  2. Build statistical and machine learning models to challenge the models in production
  3. Flex your interpersonal skills to translate the complexity of your work into tangible business goals
  4. Partner with a cross-functional team of data scientists, credit risk experts, and product managers to deliver a product customers love

Skills

Required

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

Nice to have

  • PhD in “STEM” field plus 3 years of experience in data analytics
  • 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 predictive modeling

What the JD emphasized

  • challenge the models in production
  • experience with clustering, classification, sentiment analysis, time series, and deep learning

Other signals

  • build statistical and machine learning models to challenge the models in production
  • partner with a cross-functional team of data scientists, credit risk experts, and product managers to deliver a product customers love
  • experience with clustering, classification, sentiment analysis, time series, and deep learning