Business Intelligence Data Scientist – Sr. Associate - JPMorgan Private Bank

JPMorgan Chase & Co.•New York, NY

20h

About The Position

Join the Business Intelligence team within the JPMorgan Private Bank and help shape strategy with advanced analytics and AI. You will work on high-impact initiatives that improve sales productivity, business development, and decision-making through data-driven insights. You will partner closely with business, sales, marketing, and technology teams to turn complex data into practical solutions. If you enjoy combining rigorous modeling with real-world business outcomes, this role offers the opportunity to grow your impact and help modernize how insights are delivered. As a Business Intelligence Data Scientist within the JPMorgan Private Bank Business Intelligence team, you will lead analytical initiatives that shape business strategy through data-driven insights. You will design, build, and deploy predictive models and analytics solutions using internal and external data to create actionable recommendations. You will collaborate with leaders across business, sales, and marketing to embed analytics into day-to-day decision-making and continuous improvement. You will help evolve reporting into proactive, personalized insights by prototyping and applying modern AI approaches, including machine learning and large language models.

Requirements

Bachelor’s degree in data science, computer science, statistics, mathematics, or a related technical field.
3 years of experience in data science, machine learning, or advanced analytics roles.
Advanced proficiency in Python for data analysis, modeling, and production-grade implementation.
Advanced proficiency in SQL for data extraction, transformation, and analysis.
Demonstrated ability to build, evaluate, and deploy predictive models and analytics solutions end-to-end.
Strong statistical and analytical problem-solving skills with the ability to translate complex results into actionable recommendations.
Experience designing, deploying, and operating production machine learning pipelines and services.
Working knowledge of AI implementation in software development contexts, including modernization of legacy codebases.
Ability to partner effectively across technical and non-technical teams to drive delivery and adoption.

Nice To Haves

Experience supporting sales, marketing, or productivity analytics use cases in a financial services environment.
Experience integrating external datasets and managing ongoing relationships with data providers or vendors.
Familiarity with large language model applications, evaluation approaches, and responsible AI considerations.
Experience with scalable data engineering patterns for analytics, including orchestration and automated monitoring.
Strong storytelling skills, including the ability to influence stakeholders using clear visuals and executive-ready narratives.

Responsibilities

Partner with business, sales, marketing, and technology teams to define requirements and deliver analytics solutions that drive measurable outcomes.
Design, develop, and deploy machine learning and advanced analytics solutions for complex business problems.
Apply statistical analysis, predictive modeling, and AI techniques to generate insights from large, complex datasets.
Perform exploratory data analysis to identify trends, patterns, and opportunities for growth and productivity improvements.
Communicate insights and recommendations through clear narratives, visualizations, and presentations tailored to stakeholders.
Prototype AI-enabled approaches, including large language models and automation, to deliver personalized, context-aware insights and recommendations.
Identify, evaluate, and onboard internal and external datasets to support analytics and modeling initiatives.
Assess data quality and reliability, and implement automated validation and monitoring to maintain data integrity.
Collaborate with engineering partners to implement scalable data pipelines, model deployment workflows, and analytics infrastructure.
Ensure governance, security, documentation, and lineage standards are met across data and model integration processes.
Translate business needs into clear technical specifications and contribute production-quality code across the analytics lifecycle.