Manager, Data Science - Pulse Network, Business Intelligence

Capital One Capital One · Banking · Houston, TX

Manager, Data Science role at Capital One focused on Business Intelligence and pricing strategy optimization. The role involves leveraging machine learning models throughout their development lifecycle, from design to implementation, using technologies like Python, AWS, H2O, and Spark. The ideal candidate is innovative, creative, statistically-minded, and proficient with large datasets and relational databases.

What you'd actually do

  1. Partner with a cross-functional team of pricing analysts, finance analysts, data analysts and data engineers to optimize revenue.
  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
  • 1 year of experience working with AWS
  • 4 years’ experience in Python, Scala, or R for large scale data analysis
  • 4 years’ experience with machine learning and statistical modeling techniques
  • 4 years’ experience with SQL
  • Experience with large-scale transaction datasets

What the JD emphasized

  • machine learning models through all phases of development
  • large volumes of numeric and textual data
  • machine learning and statistical modeling techniques

Other signals

  • pricing strategy
  • predictive modeling
  • large volumes of numeric and textual data
  • machine learning models through all phases of development