Manager, Data Scientist - Emerging Payments & Airkey

Capital One Capital One · Banking · New York, NY

Manager, Data Scientist role at Capital One focused on Emerging Payments & Airkey. The team builds ML models for customer-facing payment experiences, processing large-scale financial data using technologies like XGBoost, transformers, embeddings, and LLMs. The role involves building ML models from design through production for millions of customers, partnering with cross-functional teams, and leveraging various technologies like SQL, Python, AWS, and Spark.

What you'd actually do

  1. Creating machine learning models from development through testing and validation to our 30+ million customers in production
  2. Designing rich data visualizations to communicate complex ideas to customers or company leaders
  3. Investigating the impact of new technologies on the future of mobile banking and the financial world of tomorrow
  4. Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product that customers love
  5. Leverage a broad stack of technologies - SQL, Python, Conda, AWS, Spark, and more - to reveal the insights hidden within huge volumes of numeric and textual data
  6. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
  7. Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Skills

Required

  • Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics OR Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics OR PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related 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 (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data science
  • experience with transformers
  • At least 4 years of experience in Python, Scala for large scale data analysis
  • At least 4 years of experience with machine learning
  • At least 4 years of experience with SQL
  • At least 1 year of experience working with AWS

What the JD emphasized

  • machine learning models
  • customer-facing features
  • tens of millions of customers
  • large scale card and debit data
  • XGBoost, transformers, embeddings, and LLMs
  • Databricks, Snowflake
  • development through testing and validation to our 30+ million customers in production
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation

Other signals

  • machine learning models
  • customer-facing features
  • tens of millions of customers
  • large scale card and debit data
  • XGBoost, transformers, embeddings, and LLMs
  • Databricks, Snowflake
  • development through testing and validation to our 30+ million customers in production
  • Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation