Senior Data Scientist - Scotiabank, Toronto

AI Summary

This Senior Data Scientist role at Scotiabank in Toronto focuses on leading the design, development, and deployment of AI-powered software solutions for Scotia Global Asset Management. The position requires hands-on expertise in Python and SQL to build production-grade AI applications, including advanced financial modelling algorithms. Candidates need over five years of experience delivering data science or applied AI solutions in complex environments, alongside strong machine learning and statistical modelling foundations.

About the Role

As a Senior Data Scientist at Scotiabank, you will be instrumental in transforming operations across Scotia Global Asset Management by designing, developing, and deploying cutting-edge AI-powered software solutions. This individual contributor role emphasizes a hands-on approach, requiring significant time dedicated to building robust systems using Python and SQL. You will own architectural decisions for modern AI systems, including retrieval-augmented generation (RAG), agentic workflows, and sophisticated financial modelling algorithms. Your responsibilities extend to working directly with diverse end users across investment management, distribution, product development, marketing, finance, compliance, and operations. This involves translating complex business needs into production-grade AI applications, ensuring meaningful influence over the technical direction, system design, and the overall AI product roadmap. The core focus is to build and ship innovative AI products, encompassing both enhancements to existing platforms and the creation of net-new AI solutions. This development work demands writing high-quality Python and SQL for data analysis, model development, prompt engineering, and seamless system integration, owning solutions from ideation through deployment, monitoring, and continuous iteration.

Technical Leadership and Solutions Development

A key aspect of this role involves leading AI architecture decisions. You will evaluate and define the technical architecture for various AI solutions, including RAG pipelines, vector-based retrieval, agentic and workflow-oriented Generative AI systems, and advanced financial modelling and analytical algorithms. This requires assessing technical trade-offs across build vs. buy scenarios, model selection, infrastructure considerations, cost, latency, and risk. Collaboration is crucial, as you will partner with platform, security, and data teams to ensure that all solutions are scalable, secure, and compliant with Scotiabank's rigorous standards. Furthermore, a critical responsibility is translating ambiguous or complex business requirements into clear technical designs and implementation plans. This involves engaging directly with business users to understand real-world problems and workflows, then collaborating cross-functionally with product, platform, security, and governance teams to shape the strategic direction of AI product development.

Skills for Success

To succeed in this challenging and rewarding role, candidates should possess more than five years of experience delivering data science, machine learning, or applied AI solutions within large, complex environments. A strong foundation in machine learning, statistical modelling, and data science is essential, including practical experience with generative AI systems. Hands-on expertise in Python and SQL is mandatory, coupled with proven experience working with large and complex datasets. The ideal candidate will have experience designing or contributing to production AI systems, moving beyond mere experiments or proofs of concept. The ability to evaluate architectural trade-offs and make sound technical decisions aligned with business goals is critical. A degree in Mathematics, Computer Science, Engineering, Physical Sciences, or a related quantitative field, or equivalent practical experience, is expected. Comfort in collecting and incorporating user feedback for continuous product improvement, along with adaptability in an Agile environment, are also important. Strong collaboration skills are vital for working closely with engineers, designers, and product managers, alongside a curious and eager approach to learning new technologies, tools, and best practices across the industry. Scotiabank fosters an inclusive culture, valuing diverse skills and experiences.