Lead Data Scientist

Experteer Overview In this Lead Data Scientist role, you will own the full data science lifecycle to design and productionize ML solutions within RBC’s Applied AI group. You will translate ambiguous business questions into measurable ML problems and partner with engineers to deploy data-driven features in financial workflows. Expect to work on LLM-augmented analytics, data preparation, and model governance in a fast-paced, regulatory environment. You will lead a greenfield AI squad, mentor juniors, and drive impact through scalable, production-ready models. Compensation / Benefits * Own end-to-end model development: feature engineering, training, evaluation, and production handoff * Frame ambiguous business problems into clearly defined ML/AI problems with measurable success criteria * Collaborate with business partners to align solutions with actionable outcomes * Build and evaluate LLM-augmented workflows and RAG pipelines where appropriate * Prepare and transform structured and unstructured data for modeling * Design offline and online evaluation frameworks and ensure model quality pre- and post-deployment * Develop predictive models and visualizations to convey insights to stakeholders * Mentor and lead junior Data Scientists throughout the ML lifecycle * Monitor production models for drift and performance; build dashboards and report insights * Document experiments and support AI governance; present findings to stakeholders Tasks * Master’s in Computer Science (PhD in Computer Science with specialization in Data Science, Mathematics & Statistics is a must) * 10+ years IT experience with 3+ years building and deploying ML models in production * Experience with rigorous model evaluation (holdout sets, cross-validation, leakage prevention, business metric alignment) * Practical understanding of LLM capabilities and limitations; know when to use generative AI vs classical ML * Experience building or evaluating RAG pipelines or LLM-augmented analytics workflows * Comfort in enterprise LLM gateway environments (model routing, cost awareness, token management) * Experience in regulated/compliance-sensitive environments with model documentation and explainability * Strong analytical, problem-solving, time management, and organizational skills * Ability to decide when ML is necessary vs simpler rules; avoid over-engineering * Familiarity with LLM evaluation frameworks and validation strategies * Proficiency in programming (Python) and data visualization Key requirements * 401(k) with company matching * health, dental, vision, life, disability insurance * paid time off * coaching and leadership development * collaborative, high-performing team * competitive compensation

regulatory data for modeling * Design offline and online evaluation frameworks and ensure model quality pre- and post-deployment * Develop predictive models and visualizations to convey insights to stakeholders * Mentor and lead junior Data Scientists throughout the ML lifecycle * Monitor production models for drift and performance; build dashboards and report insights * Document experiments and support AI governance; present findings to stakeholders Tasks * Master’s in Computer Science (PhD in Computer Science with specialization in Data Science, Mathematics & Statistics is a must) * 10+ years IT experience with 3+ years building and deploying ML models in production * Experience with rigorous model evaluation (holdout sets, cross-validation, leakage prevention, business metric alignment) * Practical understanding of LLM capabilities and limitations; know when to use generative AI vs classical ML * Experience building or evaluating RAG pipelines or LLM-augmented analytics workflows * a models in enterprise LLM gateway environments (model routing, cost awareness, token management) * Experience in regulated/compliance-sensitive environments with model documentation and explainability * Strong analytical, problem-solving, time management, and organizational skills * Ability to decide when ML is necessary vs simpler rules; avoid over-engineering * Familiarity with LLM evaluation frameworks and validation strategies * Proficiency in programming (Python) and data visualization Key requirements * 401(k) with company matching * health, dental, vision, life, disability insurance * paid time off * coaching and leadership development * collaborative, high-performing team * competitive compensation