Lead Data Scientist

In this role as a Lead Data Scientist, you will analyze, design, and implement data science and machine learning solutions using RBC’s enterprise suite of analytics tools. The USWM Applied AI group specializes in leveraging large datasets to explore and discover new insights that traditional analytics cannot uncover. By applying leading-edge technologies and machine learning techniques, the group helps RBC understand the changing business environment, identify growth opportunities, and improve business operations. This is a senior individual contributor role on a greenfield Applied AI squad, where you will own the full data science lifecycle—from problem framing and exploratory analysis to model development, evaluation, and production performance. You will collaborate with AI engineers and MLOps to integrate models into real financial services workflows, ensuring both technical rigor and business impact.

Responsibilities

• Collaborate with business partners and stakeholders to understand objectives and frame ambiguous problems into well-defined ML/AI problem statements with measurable success criteria.
• Own end-to-end model development, including feature engineering, training, evaluation, and production handoff.
• Build and evaluate LLM-augmented workflows, combining classical ML signals with generative AI where appropriate.
• Prepare and transform structured and unstructured data, and implement statistical and ML models using Python and R.
• Design and maintain offline and online evaluation frameworks to ensure model quality before and after deployment.
• Leverage visualization tools to convey data-driven insights and actionable recommendations to stakeholders.
• Quickly learn and adapt new methods, tools, and technologies from research communities.
• Communicate findings effectively to business partners and executives.
• Develop predictive models, quantitative analyses, and visualizations for targeted big data sources.
• Lead and mentor junior Data Scientists throughout the ML lifecycle.
• Monitor production models for drift and performance, build dashboards, and communicate insights.
• Document experiments and support AI governance, presenting findings to technical and business stakeholders.

Requirements

• Master’s in Computer Science or PhD in Computer Science with a specialization in Data Science, Mathematics, or Statistics.
• 10+ years of total IT experience, with 3+ years building and deploying ML models in production environments (not just notebooks).
• Experience with model evaluation rigor, including holdout sets, cross-validation, leakage prevention, and business metric alignment.
• Practical understanding of LLM capabilities and limitations, including when to use generative AI vs. classical ML vs. deterministic rules.
• Experience building or evaluating RAG pipelines or LLM-augmented analytics workflows.
• Comfortable working within an enterprise LLM gateway environment, including model routing, cost awareness, and token management.
• Experience in regulated or compliance-sensitive environments, with a focus on model documentation, auditability, and explainability.
• Strong analytical, problem-solving, time management, and organizational skills.
• Ability to distinguish when a problem requires ML vs. a simpler rule-based approach to avoid over-engineering.
• Familiarity with LLM evaluation frameworks (e.g., RAGAS, DeepEval, LLM-as-judge) or equivalent golden dataset approaches.
• Understanding of hallucination risks and validation strategies for LLM outputs in business-critical decisions.
• Proficiency in programming, scripting languages, and data visualization.

Nice to Have

• Financial services domain experience (e.g., wealth management, portfolio analytics, risk scoring, client segmentation, or fraud detection).
• Experience with NLP pipelines for financial document understanding, summarization, or entity extraction.
• Familiarity with A/B testing and causal inference for evaluating model interventions in production.
• Experience with Databricks or Snowflake ML for large-scale feature computation and model training.
• Exposure to graph-based analytics or network analysis for relationship modeling.
• Experience with MLflow, Weights & Biases, or equivalent for experiment tracking and model registry.
• Familiarity with Linux environments and shell scripting.
• Experience with data extract, transform, and load (ETL) processes for varied data types.

Benefits

• A comprehensive Total Rewards Program, including competitive compensation and flexible benefits.
• 401(k) program with company-matching contributions.
• Health, dental, vision, life, and disability insurance.
• Paid time off.
• Leaders who support development through coaching and managing opportunities.
• Opportunities to make a difference and lasting impact in a dynamic, collaborative, and high-performing team.
• Challenging work and opportunities to build close relationships with clients.
• Potential to earn additional compensation through RBC’s discretionary variable compensation program, tied to business performance and individual goals.

Pay

The expected salary range for this position is $100,000 - $170,000, depending on experience, skills, registration status, market conditions, and business needs.

Location: Minneapolis, MN (250 Nicollet Mall). Full-time role with a 40-hour workweek.