Data Scientist Lead, Vice President

JPMorgan Chase

Confirmed live today High trust

Quick summary

Work type
On-site
Location
OH
Posted
3 days ago
Freshness
Confirmed live today

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How this pay compares to similar roles

Similar $158k
$101k most similar roles pay here $222k

This listing doesn't post a salary. Most similar roles pay $126,800–$188,500.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1151 open roles on FindRole.

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At a glance

TL;DR · Data Scientist Lead, Vice President

Data Scientist Lead, Vice President will join the team to build and evolve a high-quality measurement data foundation that enables trusted analytics and decision-making at scale. This role focuses on designing and delivering resilient datasets, pipelines, and reusable metrics to support hypothesis-driven analyses across the product development lifecycle. The individual will design and operate scalable batch and streaming data pipelines, curate trusted data products like conformed dimensions and event models, and manage workflow orchestration using Apache Airflow. Key responsibilities include modeling and transforming data for analytics using SQL and dbt while writing production-grade Python and PySpark code. The role requires expertise in Databricks, large-scale distributed data processing, and data modeling. This position addresses the challenge of measuring AI adoption and productivity outcomes by establishing robust governance, quality standards, and reliable metrics across complex systems.

What you'll do

  • Design, build, and operate scalable batch and streaming data pipelines with clear SLAs and monitoring.
  • Develop and curate trusted data products including conformed dimensions, event models, and marts.
  • Build and maintain feature-ready datasets to measure AI adoption and productivity outcomes.
  • Drive data quality and governance through validations, lineage tracking, and access controls.
  • Manage workflow orchestration using tools like Apache Airflow to schedule and monitor data movements.
  • Model and transform data for analytics using SQL and dbt to ensure repeatable measurement.
  • Write production-grade Python and PySpark code with disciplined testing and performance tuning.
  • Mentor a team of engineers while establishing engineering best practices and technical standards.

What we're looking for

  • Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
  • 5+ years of hands-on experience delivering production data solutions in a fast-paced engineering environment.
  • Strong software engineering fundamentals including system design, data structures, and object-oriented programming.
  • Strong understanding of data modeling including conceptual, logical, physical, dimensional, normalized, and event-based approaches.
  • Hands-on experience with Databricks and large-scale distributed data processing using Spark or PySpark.
  • Strong SQL skills and experience with modern transformation tooling like dbt to build maintainable codebases.
  • Experience designing and operating orchestration pipelines using Airflow or equivalent tools for monitoring and backfills.
  • Experience with lakehouse/warehouse patterns, BI semantic layers, or experimentation analytics (preferred).

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