Data Scientist

4 weeks ago


Richmond, Virginia, United States Capital One Full time

At Capital One, we're pushing the boundaries of data-driven decision-making. As a Data Scientist in our Enterprise Tech Analytics team, you'll be part of a cross-functional team that's leading the next wave of disruption. Using the latest in computing and machine learning technologies, you'll operate across billions of customer records to unlock big opportunities that help everyday people save money, time, and agony in their financial lives.

Key Responsibilities:

  • Partner with data scientists, software engineers, and product managers to deliver innovative products
  • Leverage a broad stack of technologies to reveal insights hidden within huge volumes of numeric and textual data
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  • Translate complex data insights into tangible business goals

Requirements:

  • Currently has, or is in the process of obtaining a Bachelor's Degree plus 2 years of experience in data analytics
  • At least 1 year of experience in open source programming languages for large scale data analysis
  • At least 1 year of experience with machine learning
  • At least 1 year of experience with relational databases

Preferred Qualifications:

  • Master's Degree in a STEM field or PhD in a STEM field
  • Experience working with AWS
  • At least 2 years' experience in Python, Scala, or R
  • At least 2 years' experience with machine learning
  • At least 2 years' experience with SQL

Capital One is an Equal Opportunity Employer:

Capital One is committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, disability, genetic information, marital status, sexual orientation, gender identity, gender reassignment, citizenship, immigration status, protected veteran status, or any other basis prohibited under applicable federal, state, or local law.


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