Mid-Level
Associate Data Scientist - Data Intelligence Wealth Management Solutions
Join the Solutions Data Intelligence Group and help shape the future of data and AI in Wealth Management. Leverage your modeling and technical skills to deliver impactful solutions for clients and advisors. Collaborate with senior leaders and cross-functional teams to drive innovation and business growth.
As an Associate in the Solutions Data Intelligence Group (DIG), you identify, develop, and implement high-impact data and AI opportunities. You independently build, evaluate, and refine models and analytical solutions, including AI agents and LLM-based applications. You partner with technology, operations, and business teams to deliver advanced analytics and support strategic priorities.
Job Responsibilities:
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Create the technical design for Data Intelligence use cases, including front-to-back architecture, data delivery pipelines, and build vs. buy recommendations,
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Partner with Technology, Operations, vendors, and other functions to implement use cases, with supervision from the Data Intelligence Forum (DIF)
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Handlie tactical projects involving automation and AI agents
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Partner with the DIG Data Strategists to build, score, and present use cases to the Wealth Management Solutions business
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Develop, evaluate, and refine models and analytical solutions, including defining AI evaluations to measure model quality and reliability
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Ensure ongoing successful delivery of team priorities defined by DIG team leads
Required qualifications, capabilities and skills:
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4+ years of experience in applied modeling and analytical problem solving with a strong quantitative focus
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Machine learning and programming skills (Python) with proven ability to use data and analytics to drive innovation and business results
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Hands-on experience building and deploying AI agents and/or LLM-based applications
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Ability to work independently on AI projects, managing ambiguity and driving initiatives from concept to completion
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Bachelor’s degree; advanced degree in quantitative discipline (math, statistics, economics, engineering, etc.)
Preferred qualifications, capabilities and skills:
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Exposure to LLMs and modern AI frameworks
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Experience designing and running AI evaluations (e.g., benchmarking, quality/accuracy metrics, guardrail and safety testing)
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Familiarity with AI product development lifecycle: defining requirements, prioritizing features, and shaping roadmaps in partnership with stakeholders
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Demonstrated experience working with engineering, developers, and other technology teams
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Critical thinking skills and the ability to work through problems, develop creative solutions, and act on those ideas through completion
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