Sr. Data Scientist

RBC•Minneapolis, MN

1d•Onsite

About The Position

The Senior Data Scientist is responsible for delivering AI-driven intelligence, advanced analytics, and decision-support capabilities that improve advisor productivity, client engagement, operational efficiency, and regional business growth across Wealth Management. This role partners closely with regional business leadership, advisors, Data & AI teams, and Technology partners to identify high-value opportunities and rapidly deliver practical AI and analytical solutions aligned to regional priorities.

Requirements

5+ years of experience as a Data Scientist or Analytics professional delivering machine learning and AI solutions in business environments
Strong proficiency in Python/SQL, predictive modeling, statistical analysis, and cloud-based analytics environments
Demonstrated ability to translate complex business problems into analytical and AI requirements with measurable success metrics
Deep understanding of Wealth Management business models, advisor workflows, and client engagement strategies
Strong executive communication skills with ability to simplify complex concepts and influence across business and technology teams

Nice To Haves

Experience with Generative AI, NLP, MLOps concepts, and model lifecycle management
Background in financial services and familiarity with financial products, portfolio concepts, and household analytics

Responsibilities

Lead development of AI and analytical capabilities aligned to regional Wealth Management priorities including advisor productivity enhancement, client engagement intelligence, household insights, pipeline analytics, and attrition indicators
Partner directly with regional business leadership and advisor teams to identify high-impact AI opportunities, prioritize use cases, and translate business challenges into analytical solutions
Design and embed decision intelligence capabilities directly into advisor and operational workflows, including advisor copilots, meeting preparation intelligence, and opportunity scoring
Develop and operationalize advanced analytics models including predictive models, recommendation systems, segmentation models, and NLP/GenAI-powered solutions end-to-end
Support regional AI incubation through rapid prototyping, hypothesis testing, and iterative learning with controlled experimentation and modular solution design
Present analytical findings and AI recommendations to leadership audiences and drive adoption of AI-enabled workflows
Collaborate with Data Engineering and AI Engineering teams on deployment, operationalization, and continuous monitoring of analytical solutions
Ensure all solutions align with Model Risk Management standards, data governance, privacy policies, responsible AI principles, and enterprise architecture requirements