Scientist Jobs

About The Position

Requirements

• Advanced degree in Statistics, Mathematics, Computer Science, Engineering, Data Science, Artificial Intelligence, or related field; or Bachelor’s degree with equivalent technical experience.
• Minimum of 5 years of applicable experience in data science, advanced analytics, machine learning, or AI solution development.
• Advanced programming capability in Python, R, or similar analytical programming languages; strong SQL experience preferred.
• Advanced understanding of programming, statistics, machine learning, exploratory data analysis, data visualization, model production, insight generation, and data wrangling.
• Deep knowledge of statistical modeling, machine learning algorithms, experimental design, model evaluation, and analytical storytelling.
• Experience leading complex data science initiatives from discovery through implementation.
• Working knowledge of generative AI, LLMs, prompt engineering, embeddings, RAG, AI evaluation, or agentic AI concepts.
• Ability to influence stakeholders, develop business cases, and communicate complex technical concepts to senior audiences.
• Experience mentoring data scientists and promoting technical best practices.

Nice To Haves

• Experience with Azure, Databricks, Git, MLflow, model monitoring, or cloud-based AI/ML development environments.
• Familiarity with LLMOps, MLOps, model governance, Responsible AI, privacy, security, and model risk management.
• Experience with vector databases, semantic search, embeddings, RAG pipelines, or knowledge retrieval architectures.
• Exposure to LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar agentic AI frameworks.
• Experience supporting AI product development, experimentation portfolios, proof-of-concepts, MVPs, or scaled enterprise deployments.
• Experience in financial services, insurance, or another regulated environment.

Responsibilities

• Lead complex analytics, machine learning, and generative AI initiatives from problem framing through implementation.
• Define analytical approaches, model strategies, evaluation methods, and success measures for high-impact AI use cases.
• Guide experimentation with AI assistants, copilots, RAG solutions, and agentic workflows in partnership with Agentic Engineers and technology teams.
• Apply advanced statistical modeling, machine learning, experimentation, and AI evaluation techniques to solve business problems.
• Translate complex analytical findings and AI outputs into clear, implementable recommendations for business stakeholders and leaders.
• Prepare business cases for assigned initiatives, including expected benefits, adoption considerations, risks, and lessons learned from related business contexts.
• Develop an intimate understanding of supported business areas, including value chains, customer journeys, operating context, and company culture.
• Anticipate and proactively address peer review, governance, and model risk management considerations through clear documentation and defensible analytical choices.
• Provide guidance and mentorship to junior data scientists, facilitate best-practice exchange, and share technical expertise across the team.
• Contribute reusable approaches for experimentation, model validation, and AI solution assessment within assigned initiatives and the broader data science practice.
• Build a strong internal network across business, data science, engineering, platform, and governance partners, and share lessons learned to advance practice maturity.

Benefits

• health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans.
• various retirement savings plans (including pension and a global share ownership plan with employer matching contributions)
• financial education and counseling resources.
• generous paid time off program in Canada includes holidays, vacation, personal, and sick days, and we offer the full range of statutory leaves of absence.