Data Science Lead & VP: Architect of Scalable Analytics

JPMorganChase; Chicago (IL); On-site; USD 133,000 - 175,000; Full time

Job summary

JPMorganChase in Chicago seeks a Data Scientist Lead to guide data pipelines, metrics, and analytics at scale. You will own trusted datasets, implement robust governance, and deliver measurable outcomes across PDLC with a hands‑on approach in Python and PySpark.

The role emphasizes collaboration with analytics, product, and engineering teams, drive for reliability, and leadership of a small team to establish engineering standards and best practices that improve observability and cost efficiency.

Qualifications

• 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 design, data structures, and testing strategies.

Responsibilities

• Design, build, and operate scalable data pipelines with clear SLAs and monitoring.
• Develop and curate trusted data products with documentation and ownership.
• Build and maintain metrics and datasets to measure AI adoption and productivity outcomes.
• Drive data quality and governance with validations, lineage, and access controls.
• Develop and operate workflow orchestration (Airflow) to schedule and monitor data movement.
• Model and transform data using SQL/dbt to support trusted reporting.
• Write production‑grade Python/PySpark with testing and performance tuning.
• Partner with stakeholders to define requirements and success criteria.
• Establish and enforce engineering practices and improve observability and cost/performance.
• Mentor a team of 2 and influence technical direction.

Skills

Databricks

SQL

Python

PySpark

dbt

Airflow

Education

Bachelor’s degree in CS/Engineering

Tools

Databricks

Airflow

dbt

Job description

JPMorganChase in Chicago seeks a Data Scientist Lead to guide data pipelines, metrics, and analytics at scale. You will own trusted datasets, implement robust governance, and deliver measurable outcomes across PDLC with a hands‑on approach in Python and PySpark.

The role emphasizes collaboration with analytics, product, and engineering teams, drive for reliability, and leadership of a small team to establish engineering standards and best practices that improve observability and cost efficiency.