Lead Data Scientist

Royal Bank of Canada•Edina, MN

5d•$100,000 - $170,000•Onsite

About The Position

In this role as a Lead Data Scientist you will analyze, design and implement data science / machine learning solutions using RBC’s enterprise suite of analytics tools. USWM Applied AI group specializes in taking full advantage of large data sets to explore and discover new insights that would have not been possible with traditional analytics. Leveraging leading edge technologies and capabilities, the group applies machine learning and statistical modelling techniques to help RBC understand the changing business environment, discover new growth opportunities and determine where business improvements can be made. This is a senior individual contributor role on a greenfield Applied AI squad. You will own the full data science lifecycle — from problem framing and exploratory analysis through model development, evaluation, and production performance. You'll work alongside AI engineers and MLOps to bring models and data-driven features into real financial services workflows. This isn't a notebook-and-dashboard role: you write production Python, collaborate closely with engineering, and take clear ownership of model quality and business outcomes. Financial domain knowledge, statistical rigor, and the ability to translate ambiguous business questions into solvable ML problems are equally important as technical depth.

Requirements

Master’s in computer science or PHD in Computer Science with Specialization in Data Science, Mathematics & Statistics.
10+ years total IT experience with 3+ years building and deploying ML models in production environments — not just notebooks
Experience with model evaluation rigor — holdout sets, cross-validation, leakage prevention, business metric alignment
Practical understanding of LLM capabilities and limitations — knows when to use generative AI vs. classical ML vs. deterministic rules
Experience building or evaluating RAG pipelines or LLM-augmented analytics workflows — even if not the primary architect
Comfortable working within an enterprise LLM gateway environment — model routing, cost awareness, token management
Worked in a regulated or compliance-sensitive environment — model documentation, auditability, and explainability requirements
Excellent analytical, problem solving, time management and organizational skills.
Can distinguish when a problem needs ML vs. a simpler rule-based approach — avoids over-engineering
Familiarity with LLM evaluation frameworks — RAGAS, DeepEval, LLM-as-judge, or equivalent golden dataset approaches.
Understands hallucination risks and validation strategies for LLM outputs used in business-critical decisions.
Comfortable working within an enterprise LLM gateway environment — model routing, cost awareness, token management.
Experience in programming, scripting languages and data visualization.

Nice To Haves

Financial services domain — wealth management, portfolio analytics, risk scoring, client segmentation, or fraud detection experience.
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.
Databricks or Snowflake ML for large-scale feature computation and model training
Exposure to graph-based analytics or network analysis for relationship modeling
MLflow, Weights & Biases, or equivalent for experiment tracking and model registry
Familiar with a Linux environment and shell scripting.
Familiar with data extract, transform, and load processes with a variety of data types.

Responsibilities

Collaborate with key business partners and stakeholders to understand business objectives/opportunities and problem statements in order to provide solutions that align to business needs that are actionable with a tangible outcome
Frame ambiguous business problems into well-defined ML and AI problem statements with measurable success criteria.
Own end-to-end model development — 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 data (structured/non-structured)
Design and maintain offline and online evaluation frameworks — ensuring model quality before and after deployment
Prepare, integrate large and varied datasets and implement statistical and ML models using Python and R.
Leverage visualization tools/packages to story-tell and to convey data-driven insights with actionable recommendations to key stakeholders
Quickly learn new methods, tools and technologies presented in research communities to implement, adapt and innovate
Effectively communicate findings to business partners and executives.
Developing predictive data models, quantitative analyses and visualization of 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. Present findings to technical and business stakeholders.