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

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. J
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.

Job 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. J
• 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.

Required qualifications, capabilities, and skills

• 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.

Preferred qualifications, capabilities, and skills

• 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.