Data Science Vice President - Card Data Analytics

JPMorgan Chase JPMorgan Chase · Banking · Wilmington, DE +1 · Consumer & Community Banking

Data Science Vice President at JPMorgan Chase within the Card Data & Analytics team, responsible for developing and implementing AI/ML solutions, including Generative AI and foundation models, to drive strategic initiatives and business impact within the credit card business. The role involves end-to-end problem-solving, from translating business questions to building models and communicating insights.

What you'd actually do

  1. Leverage experience and analytical skills to uncover novel use cases of Big Data analytics, including opportunities to responsibly apply foundation models and Generative AI
  2. Drive data science and analytics strategies, including recommendations on analytical products and standards
  3. Help partners define business problems and scope analytical solutions
  4. Build an understanding of problem domains and available data assets
  5. Research, design, implement, and evaluate analytical approaches and models, including Generative AI-based methods

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Bachelor’s degree in a relevant quantitative field and 5+ years of data analytics experience, or advanced degree and 2+ years of experience
  • Exceptional analytical, quantitative, problem-solving, and communication skills
  • Intellectual curiosity for solving business problems
  • Leadership and collaboration skills
  • Knowledge of statistical software (for example, Python, R, SAS) and data querying languages (for example, SQL)
  • Familiarity with Generative AI and prompt engineering basics (prompt design, evaluation, guardrails)
  • Experience with modern analytics tools (for example, SAS, SQL, Hive, Hadoop, Spark, Python, Tableau, Alteryx)
  • Ability to convey complex information to technical and non-technical audiences

Nice to have

  • Experience with large language model-enabled applications such as retrieval-augmented generation, classification or extraction from unstructured text, or agent-like workflows; exposure to evaluation methods for quality, cost, and latency
  • Understanding of key drivers within the credit card profit and loss statement
  • Financial services background
  • Master of Science degree or equivalent

What the JD emphasized

  • responsible
  • scalable
  • responsible
  • evaluation methods for quality, cost, and latency

Other signals

  • Leverage experience and analytical skills to uncover novel use cases of Big Data analytics, including opportunities to responsibly apply foundation models and Generative AI
  • Research, design, implement, and evaluate analytical approaches and models, including Generative AI-based methods
  • Familiarity with Generative AI and prompt engineering basics (prompt design, evaluation, guardrails)
  • Experience with large language model-enabled applications such as retrieval-augmented generation, classification or extraction from unstructured text, or agent-like workflows; exposure to evaluation methods for quality, cost, and latency