Manager, Data Scientist - Credit Review

Capital One Capital One · Banking · McLean, VA +5

Capital One is seeking a Manager, Data Scientist for their Credit Review Models, Data and Innovative solutions team. The role involves building statistical and machine learning models to challenge existing production models, leveraging technologies like Python, AWS, and Spark. The candidate will partner with cross-functional teams to deliver innovative solutions in risk management and enterprise decision-making. The role requires a strong statistical background, experience with machine learning, and proficiency in open-source tools and cloud platforms.

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
  • built models, validated them, and backtested them
  • experience with clustering, classification, sentiment analysis, time series, and deep learning

Other signals

  • building statistical and machine learning models
  • challenging models in production
  • leveraging open source programming languages for large scale data analysis
  • working with machine learning
  • utilizing relational databases