What are the responsibilities and job description for the Data Science Senior Associate - Card Data & Analytics team position at JPMorgan Chase?
As a Data Science Senior Associate in the Card Data & Analytics team, you will develop AI/ML solutions that drive the bottom line for our Credit Card business. You will utilize your skills in data analytics, consulting, and programming to support strategic initiatives and deliver actionable insights. Collaborate with partners across the Card business to define problems, scope solutions, and deliver high-quality analytical models. Your work will involve a mix of consulting, data science, and programming, with a focus on driving data science and analytics strategies.
Job Responsibilities:
- Leverage experience and analytical skills to uncover novel use cases of Big Data analytics, including opportunities to responsibly apply foundation models and Generative AI.
- Drive data science and analytics strategies, including recommendations on analytical products and standards.
- Help partners define business problems and scope analytical solutions.
- Build an understanding of problem domains and available data assets.
- Research, design, implement, and evaluate analytical approaches and models, including GenAI-based methods.
- Perform exploratory statistics and data mining tasks on diverse datasets.
- Communicate findings and obstacles to stakeholders to drive delivery to market.
- Develop subject matter expertise in financial and operational domains.
- Code solutions with strong programming skills.
- Collaborate across teams to deliver the best solutions for clients.
Required Qualifications, Capabilities, and Skills:
- Bachelor’s degree in a relevant quantitative field and 3 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 (e.g., Python, R, SAS) and data querying languages (e.g., SQL).
- Familiarity with GenAI and prompt engineering basics (prompt design, evaluation, guardrails).
- Experience with modern analytics tools (e.g., SAS, SQL, Hive, Hadoop, Spark, Python, Tableau, Alteryx).
- Ability to convey complex information to technical and non-technical audiences.
Preferred Qualifications, Capabilities, and Skills:
- Experience with LLM-enabled applications such as retrieval-augmented generation, classification or extraction from unstructured text, or agent-like workflows; exposure to evaluation methods for LLM quality, cost, and latency.
- Understanding of key drivers within the credit card P&L.
- Financial services background preferred.
- M.S. degree or equivalent.