Data Scientist Lead, Vice President

JPMorgan Chase & Co. • Columbus, OH

2d • $133,000 - $175,000

About The Position

Chase is seeking a Data Scientist Lead to build and evolve a high-quality measurement data foundation that supports trusted analytics and decision-making at scale. The role designs and delivers resilient datasets, pipelines, and reusable metrics for hypothesis-driven analyses and experiments across the product development lifecycle (PDLC).

The position combines hands-on data engineering with setting engineering standards and improving reliability, observability, and usability across the data lifecycle. It supports clear measurement of value, friction, and operating-model changes as teams become more agentic. The role also provides technical direction and mentorship to a team of two.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
• At least 5 years of hands-on experience delivering production data solutions in a fast-paced engineering environment, actively coding and owning outcomes.
• Strong software engineering fundamentals, including system design, data structures, object-oriented programming, testing strategies, and the end-to-end development lifecycle.
• Strong understanding of conceptual, logical, and physical data modeling, including dimensional, normalized, and event-based approaches.
• Hands-on experience with Databricks and large-scale distributed data processing and performance tuning using Spark/PySpark.
• Strong SQL skills and experience with modern transformation tooling such as dbt, including building maintainable, testable data codebases.
• Experience designing and operating orchestration pipelines using Airflow or an equivalent, including backfills, retries, and operational monitoring.
• Demonstrated rigor building and maintaining trusted metrics, including definitions, edge cases, validation/testing, and documentation, and keeping them reliable as upstream sources change.
• Demonstrated ability to lead delivery in complex environments with multiple stakeholders and ambiguous requirements.

Nice To Haves

• Experience with modern lakehouse/warehouse patterns and broader cloud data platforms, such as Databricks or 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 is not required.

Responsibilities

• Design, build, and operate scalable batch and/or streaming data pipelines with clear SLAs, monitoring, and incident response practices.
• Develop and curate trusted data products, including conformed dimensions, event models, and marts, with strong documentation and clear ownership.
• Build and maintain well-defined metrics and feature-ready datasets to measure AI adoption and productivity outcomes, including reusable aggregates, cohorting, time-windowed measures, and change control as definitions evolve.
• Drive data quality and governance through validations, reconciliations, lineage, access controls, retention, and auditability aligned to requirements.
• Develop and operate workflow orchestration, such as Apache Airflow, to schedule, monitor, and manage data movement and transformations.
• Model and transform data for analytics using SQL and dbt to support trusted reporting and repeatable measurement.
• Write production-grade Python/PySpark with disciplined testing, performance tuning, and maintainable design.
• Partner with analytics, product, and engineering stakeholders to define requirements, success criteria, and consistent interpretation of key measures, including inputs spanning finance business cases, PDLC/SDLC tools, and AI tool logs.
• Establish and enforce engineering best practices, including version control, code review, testing strategy, deployment processes, and runbooks.
• Continuously improve observability and cost/performance across freshness, completeness, timeliness, scalability, and spend.
• Mentor and develop a team of two, influencing technical direction through standards, reviews, and knowledge sharing.

Benefits

• Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior