Data Scientist, Senior Associate

JPMorgan Chase & Co.; Kentucky; On-site; USD 95,000 - 135,000; Full time

Job summary

JPMorgan Chase & Co. in Kentucky is seeking a Data Science Senior Associate to design, build, and operate resilient data pipelines and reusable metrics across the product development lifecycle.

You will deliver trusted data products, enforce data quality and governance, and mentor peers while collaborating with cross-functional teams in finance, product, and analytics. The role emphasizes hands-on development of scalable data solutions, with exposure to Airflow, dbt, Databricks, and PySpark, and

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
• 3+ years building production data solutions with ownership and delivery.
• Strong engineering fundamentals including OOP, testing, and SDLC.
• Strong data modeling skills (dimensional, normalized, event-based).
• Experience with Databricks and Spark/PySpark, SQL, and dbt.

Responsibilities

• Build and operate scalable batch/streaming pipelines with SLAs, monitoring, and incident response.
• Create and maintain trusted data products with clear ownership and docs.
• Deliver metrics and datasets for AI adoption and performance measurement.
• Implement data quality, governance controls, lineage, and retention.
• Orchestrate workflows in Airflow with backfills and retries.
• Model/transform data using SQL and dbt for trusted reporting.
• Write production-grade Python/PySpark with tests and performance tuning.
• Collaborate with stakeholders to define requirements and metric interpretation.
• Contribute to engineering best practices and improve observability and cost/perf.
• Mentor peers through reviews and knowledge sharing.

Skills

Data modeling

SQL

Python/PySpark

OOP & testing

Collaboration

Education

Bachelor’s degree in CS/Engineering

Tools

Databricks

Airflow

dbt

Spark/PySpark

CI/CD tooling

Job description

Job Description

We are seeking a Data Science Senior Associate focused on building and operating resilient datasets, pipelines, and reusable metrics that support hypothesis-driven analyses and experiments across the product development lifecycle (PDLC)

In this role, you will be hands-on in designing, developing, and maintaining data products that are reliable, observable, and well-documented—enabling partners across product, engineering, and analytics to measure what’s driving value, where friction exists, and how operating-model changes impact outcomes as teams adopt more agentic ways of working. You’ll contribute to engineering standards and help raise the quality bar through strong delivery and collaboration.

Job Responsibilities

• Build and operate scalable batch/streaming pipelines with SLAs, monitoring, and incident response participation (as needed).
• Create and maintain trusted data products (dimensions, event models, marts) with clear ownership and documentation.
• Deliver metrics and feature-ready datasets for AI adoption/productivity measurement; manage definition changes over time.
• Implement data quality and governance controls (validation, reconciliation, lineage, access, retention, auditability).
• Orchestrate workflows in Airflow (or equivalent), including backfills and retries.
• Model/transform data using SQL and dbt (or equivalent) for trusted reporting and repeatable measurement.
• Write production-grade Python/PySpark with 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 (version control, code review, CI/CD, runbooks) and improve observability and cost/performance.
• Mentor peers through reviews, documentation, and knowledge sharing (no formal people management).

Required Qualifications

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

Preferred Qualifications

• Experience with modern lakehouse/warehouse patterns and broader cloud data platforms (e.g., Databricks, Snowflake).
• Experience with BI/semantic layers and metrics management practices.
• Exposure to experimentation or hypothesis-driven analytics approaches (e.g., measurement design to support 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).