Lead Data Scientist, Next Best Action

Job Description

Job Description What is the opportunity? The Next Best Action (NBA) Machine Learning team of Personal Banking is seeking a seasoned, passionate and innovative Lead Data Scientist. In this role, you will lead efforts to solve complex AI/ML problems, analyze data, design solutions, and implement and monitor machine learning applications using cutting-edge tools and algorithms. Our mission is to remain at the forefront of the industry by enhancing client relationships and engagement through the execution of our NBA Strategy. To achieve this, we harness RBC’s extensive data assets and advanced AI capabilities to drive impactful client communications. What will you do? Collaborate with RBC internal partners to define use cases and design tailored AI solutions that address business needs . Prepare, parse, and integrate large structured and unstructured datasets for model training and inference while ensuring data privacy and unbiased outcomes. Design, develop, and operationalize state-of-the-art machine learning models, including feature engineering, model deployment, monitoring, and maintenance. Lead the migration of models to cloud environments and expand the NBA System's marketing actions and machine learning capabilities. Continuously validate and improve AI model performance to deliver innovative, scalable solutions. Research and evaluate emerging technologies to drive innovation and maintain RBC's leadership in AI-driven client engagement. Mentor junior data scientists and foster a collaborative, high-performing team environment. What do you need to succeed? Must-have Master's in computer science , Computer Engineering or similar field. 4 years of hands-on experience applying Machine Learning , preferably in Customer Marketing initiatives . Experience in executing end-to-end machine learning lifecycle. Proficiency in Python and libraries like PySpark , TensorFlow / PyTorch , and Scikit-learn. Experience with big data ecosystems (e.g., Hadoop/Spark) and cloud platforms (e.g., AWS, Azure, GCP) Understanding and hands-on experience of interpretability tools (e.g., SHAP). Proficiency in writing and understanding complex SQL queries for data manipulation and feature engineering. Strong interpersonal and communication skills to convey technical insights to diverse audiences. Passion for ethical AI, including algorithm transparency and interpretability. Nice to have Experience deploying machine learning models into production using MLOps tools such as Airflow (e.g., DAGs), MLflow , and Kubeflow . Familiarity with financial services data and regulatory compliance. Expertise in running A/B tests and causal inference methods. What’s In For You? We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual. A compreh