Lead Data Scientist, GFT

• Lead by example by designing, implementing, and optimizing advanced statistical and machine learning models from ideation through production deployment, solving real-world risk management challenges with rigor and innovation.
• Push the adoption of next-generation AI by spearheading the development of LLM-powered solutions, RAG systems, and generative AI applications that transform risk identification, automate complex workflows, and unlock new business value.
• Mentor and develop emerging talent in the data science and ML engineering community, fostering a culture of curiosity, experimentation, and technical excellence.
• Translate data into decisions by collaborating with product, business, and technology teams to identify high-impact opportunities, refine hypotheses, test assumptions, and transform complex analytical findings into clear, actionable recommendations that drive strategic decisions.
• Champion modern ML practices like responsible AI frameworks, model governance, MLOps automation, A/B testing, and reproducible research workflows to keep RBC at the forefront of data science innovation.
• Design and deploy scalable solutions from architectural decisions to hands-on model development, ensuring solutions meet the highest standards of accuracy, performance, interpretability, safety and business impact.
• Collaborate across the organization to identify requirements, scope data science initiatives, and build strong partnerships with stakeholders across business lines and technology teams.
• Continuously explore emerging technologies and methodologies, staying ahead of the curve on LLM advancements, fine-tuning techniques, transfer learning, and other cutting-edge approaches to keep RBC competitive in AI.
• Drive innovation through experimentation, leveraging research rigor to test novel ML approaches, prototype proofs-of-concept, and validate business hypotheses before large-scale deployment.
• Work directly with business and senior management to implement the vision for next-generation AI-powered solutions that create competitive advantage in risk management.

• Degree in Computer Science, Statistics, Mathematics, Engineering, or related field with demonstrated expertise in machine learning and data science fundamentals. Master's degree or PhD is a plus.
• 6+ years of hands-on experience developing, training, and deploying machine learning models and data science solutions in production environments.
• Expert-level proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, Scikit-learn, Huggingface) with the ability to write clean, reproducible, production-grade code.
• 2+ years of hands-on experience with LLMs and generative AI including transformers, fine-tuning, RLHF, prompt engineering, RAG systems, or similar GenAI technologies.
• Strong foundation in statistical modeling and experimentation including hypothesis testing, A/B testing, causal inference, and the ability to design rigorous ML experiments.
• Experience designing and implementing end-to-end ML solutions from problem definition and feature engineering through model training, validation, hyperparameter tuning, and production deployment.
• Proficiency in ML pipeline development and MLOps including data preprocessing, feature engineering, model monitoring, retraining strategies, and deployment automation.
• Understanding of responsible AI practices including model interpretability, bias detection, fairness considerations, and governance frameworks.
• Strong problem-solving and analytical skills with the ability to navigate ambiguity, ask the right questions, and develop innovative solutions to complex business and technical challenges.
• Ability to mentor and guide junior data scientists in technical and best practice areas, fostering a culture of continuous learning and excellence.
• Excellent communication skills with the ability to translate complex technical concepts into clear business insights and compelling narratives for diverse audiences from engineers to executives.
• Passion for AI innovation and a love of data-driven problem solving.

• Experience with risk management, financial services, or regulated industries with familiarity with compliance, risk, and regulatory considerations.
• Knowledge of distributed data systems like Spark, Hadoop, or cloud data warehouses such as Snowflake or BigQuery.
• Experience with time-series forecasting, anomaly detection, or recommendation systems in production environments.
• Familiarity with ML governance tools and platforms like MLflow, Weights and Biases, SageMaker, or similar.
• Published research, open-source contributions, or demonstrated thought leadership in ML and AI domains.
• Experience building and scaling high-performing data science teams across geographies.
• Exposure to reinforcement learning, causal ML, or other advanced ML methodologies.
• Understanding of software engineering best practices including version control, testing, CI/CD pipelines, and documentation standards.

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, and stock where applicable.
• Leaders who support your development through coaching and mentoring opportunities.
• Ability to make a difference and lasting impact on risk management across the organization through transformative AI solutions.
• Work in a dynamic, collaborative, and high-performing team of data scientists, ML engineers, and business partners.
• Opportunities for published research and thought leadership including presenting at conferences and contributing to the broader ML community.
• Opportunities to do challenging work, take on progressively greater accountabilities, and build close relationships with business stakeholders.

Royal Bank of Canada