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Data Scientist Senior Associate

Open 23d reposted 2× · 2 open copies

Summary

Senior Data Scientist at JPMorgan Chase building AI-driven segmentation models and analytics for co-branded credit card partnerships using Python, Databricks, and Tableau.

Join the Cards Data & Analytics team at JPMorgan Chase, where we leverage data science, research, and business acumen to deliver impactful solutions.

As a Data Scientist Senior Associate within the Co-Brand Card Analytics team, you will be responsible to lead projects that integrate agentic workflows, business intelligence platforms, and advanced analytics solutions. You will own end-to-end delivery of segmentation models, strategic analyses, and AI-driven insights that inform partnership strategies and portfolio decisions. Utilizing tools such as Databricks, Python, Tableau and Alteryx, you will develop data pipelines and dashboards, prototype AI-driven solutions, and translate complex data into strategic recommendations for Co-Brand business partners.

Job Responsibilities

  • Be part of the Segmentation and Strategic Analytics team and drive analytics-driven business strategy and growth for Co-Brand Card partnerships.
    Learn our business and how JPMorgan delivers value for our customers and partners through Co-Brand credit card products.
  • Leverage your technical skillset, domain expertise, and curiosity to identify opportunities to help grow our Co-Brand Cards business through advanced segmentation and strategic analytics.
  • Build and rigorously test new data and AI driven insights, owning the end-to-end analytical lifecycle from hypothesis to production-ready deliverables.
  • Develop scalable frameworks for seamless AI model integration across business applications. Ensure solutions can adapt and scale to support evolving Co-Brand partnership needs.
  • Build and test AI agents. Iterate designs to enhance functionality and user experience. Conduct rigorous testing for reliability and mentor junior team members on best practices.
  • Use tools like Databricks, Python, Tableau and Alteryx to create data pipelines and dashboards. Support AI-driven insights and business recommendations for Co-Brand strategic initiatives.
  • Monitor AI model performance. Identify areas for enhancement. Implement updates to maintain quality and relevance. Drive continuous improvement of analytical methodologies.
  • Lead workstreams on agile teams to support data-driven decision-making, customer segmentation, and strategic partner relationship management.

Required Qualifications, Capabilities, and Skills

  • Degree in a scientific field (Computer Science, Engineering, Data Science, Statistics, Mathematics, etc.) with 6+ years of experience in AI/ML or Data Science.
  • Experience working in Card, Co-Brand, or other Financial Services verticals.
  • Strong understanding of AI models, including large language model (LLM) capabilities and limitations, and experience applying them to business problems.
  • Proven experience with statistical analysis, data-driven decision-making, customer segmentation, and pattern identification.
  • Strong creative problem-solving skills with the ability to translate complex analytical findings into actionable business recommendations.
  • Proficiency in Python and SQL with demonstrated ability to build production-quality analytical solutions.
  • Experience with cloud platforms such as AWS, GCP, or Azure, and SDLC concepts.
  • Strong communication and collaboration skills, with the ability to present findings to senior stakeholders and work effectively across teams.
  • Experience building and contributing to agentic AI architectures and automation frameworks.

Preferred Qualifications, Capabilities, and Skills

  • Experience in customer segmentation, strategic analytics, or related fields within financial services.
  • Hands-on experience with AWS, Databricks for data management, large-scale data processing, and advanced analytics.
  • Understanding of quality assurance practices, model validation, and the importance of data integrity in production environments.

Deep knowledge of machine learning/data science theory, techniques, and tools with experience deploying models at scale.

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