You will join a collaborative team that applies advanced modeling, machine learning, and applied AI to profile clients across all lines of business, improve customer experience, and enable business performance at scale. You will take on the toughest analytical challenges the team faces—from customer segmentation to digital marketing to AI-enabled and agentic alert systems—and turn them around with speed and rigor. You will work on high-visibility initiatives, partner closely with business and technology teams, and help move ideas from concept into implemented solutions embedded in day-to-day workflows. You will also have opportunities to grow through mentoring, training, and mobility.

As a Data Scientist Associate Senior at JPMorganChase within the Consumer & Community Banking Data and Analytics organization, you will operate as a senior technical contributor and force-multiplier for the team. You will independently own end-to-end modeling solutions—framing ambiguous problems, building and validating models, and driving them into production—while helping set technical direction and elevating the work of others. You will ensure solutions are statistically sound, aligned to business objectives, delivered quickly, and integrated into business processes to drive adoption and measurable impact.

Job responsibilities

• Own the team's highest-priority modeling problems end to end, framing ambiguous questions and delivering rigorous solutions on tight timelines
• Design, build, validate, and deploy advanced models spanning customer segmentation, digital marketing, and AI-enabled and agentic alert systems
• Translate business objectives into well-scoped modeling problems with clear success criteria and measurable outcomes
• Apply machine learning, statistical, and mathematical methods to develop novel solutions—including unsupervised methods such as PCA and k-means, supervised methods such as regression, tree-based models, and gradient-boosted algorithms, as well as causal inference, forecasting, and simulations
• Build applied AI and agentic solutions, including large language model-based systems, prompt engineering, and developer and AI assistants
• Serve as a technical lead on complex workstreams, helping direct and prioritize team efforts and unblocking others
• Communicate results, insights, and recommendations clearly to business stakeholders, technology partners, and senior leadership
• Partner with data science, engineering, product, and design teams to ensure solutions are feasible, scalable, and production-ready
• Mentor and guide teammates on modeling approaches, code quality, and analytical rigor
• Manage multiple engagements by prioritizing work, tracking dependencies, and escalating risks and tradeoffs proactively

Required qualifications, capabilities, and skills

• Formal training or certification on data science concepts and 3+ years applied experience
• Undergraduate degree in a quantitative discipline or equivalent practical experience
• Strong programming skills in Python, including the modern data science and machine learning ecosystem
• Deep machine learning expertise across the full model lifecycle—feature engineering, training, validation, deployment, and monitoring
• Strong statistical and mathematical foundation, with the ability to select and apply the right technique to novel and ambiguous problems
• Applied AI engineering experience, including building large language model-based and/or agentic solutions and prompt engineering
• Proficiency with SQL and data querying at scale
• Demonstrated ownership mindset and ability to operate independently in ambiguous, fast-moving problem spaces
• Strong written, verbal, and presentation skills, with experience communicating effectively with both business and technology audiences, including senior leadership
• Ability to guide, mentor, and influence teammates and cross-functional partners

Preferred qualifications, capabilities, and skills

• Graduate degree (Master's or PhD) in a quantitative discipline
• Experience in financial services or retail banking
• Experience with modern data platforms such as Snowflake or Databricks
• Experience productionizing models and integrating them into business workflows
• Familiarity with experimentation, model lifecycle management, and machine learning operations practices