Manager, Data Science

Capital One Capital One · Banking · Plano, TX

Manager, Data Science role at Capital One in Plano, TX. This position involves partnering with cross-functional teams to deliver products, leveraging technologies like Python, AWS, and Spark. The core responsibilities include building machine learning models through all development phases (design, training, evaluation, validation, implementation) and translating complex work into business goals. Requires a quantitative background with experience in data analytics, machine learning, and relational databases.

What you'd actually do

  1. Exercising discretion at a more senior level, 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. Translate the complexity of your work into tangible business goals.

Skills

Required

  • bachelor's or foreign equivalent degree in Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, Engineering or a related quantitative field and 6 years of experience in performing data analytics
  • master's or foreign equivalent degree in the aforementioned fields or an MBA or foreign equivalent degree in a quantitative concentration and 4 years of experience
  • PhD or foreign equivalent degree in the aforementioned fields and 1 year of experience
  • leveraging open-source programming languages for large-scale data analysis
  • working with machine learning
  • utilizing relational databases

Nice to have

  • Python
  • Conda
  • AWS
  • H2O
  • Spark

What the JD emphasized

  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.

Other signals

  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
  • 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.