Data Scientist, Senior Associate

Join JPMorganChase in Columbus, OH to design and run resilient data products, pipelines, and reusable metrics that support hypothesis-driven analysis across the product development lifecycle (PDLC).

Responsibilities

• Build and operate scalable batch and streaming pipelines with SLAs, monitoring, and incident response participation (as needed).
• Design, develop, and maintain trusted data products, including dimensions, event models, and marts, with clear ownership and documentation.
• Deliver metrics and feature-ready datasets to support AI adoption and productivity measurement; manage metric definition changes over time.
• Implement data quality and governance controls such as validation, reconciliation, lineage, access, retention, and auditability.
• Orchestrate workflows in Airflow (or equivalent), including backfills and retries.
• Model and transform data using SQL and dbt (or equivalent) for trusted reporting and repeatable measurement.
• Write production-grade Python and/or PySpark, including testing, performance tuning, and maintainable design.
• Partner with cross-functional stakeholders to define requirements, success criteria, and metric interpretation across finance, PDLC/SDLC, and AI tool logs.
• Contribute to engineering best practices including version control, code review, CI/CD, and runbooks, improving observability and cost/performance.
• Mentor peers through reviews, documentation, and knowledge sharing; no formal people management.

Requirements

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• 3+ years building production data solutions with strong ownership and delivery.
• Strong engineering fundamentals including OOP, testing, and the development lifecycle.
• Strong data modeling skills across dimensional, normalized, and event-based approaches.
• Experience with Databricks and/or Spark/PySpark.
• Strong SQL experience; experience with dbt (or equivalent) and building testable data codebases.
• Experience operating orchestration pipelines using Airflow (or equivalent).
• Proven ability to build and maintain reliable metrics as sources and definitions evolve.
• Effective delivery in ambiguous, multi-stakeholder environments.

Preferred Qualifications

• Experience with modern lakehouse/warehouse patterns and broader cloud data platforms (examples include Databricks, Snowflake).
• Experience with BI / semantic layers and metrics management practices.
• Exposure to experimentation or hypothesis-driven analytics approaches, such as measurement design for tests, rollouts, and pre/post evaluation (deep causal specialization not required).
• Experience improving observability (data freshness/SLA monitoring, lineage, alerting) and contributing to operational maturity (runbooks, incident follow-ups).

Technologies

• Airflow, SQL, dbt, Python, PySpark, Databricks, Spark, OOP, CI/CD

Team Overview

• Consumer & Community Banking (CCB) serves Chase customers across personal banking, credit cards, mortgages, auto financing, investment advice, small business loans, and payment processing.
• The CCB Data & Analytics team leverages data across Chase to create advantages for business while providing value and protection for customers.
• The team spans data governance and strategy, reporting, data science, and machine learning.
• Close partnership with Technology to support cutting edge data and analytics infrastructure.