Machine Learning Analyst

Job Description Please note Current work authorization for Canada is required for all openings. You will be working on a flexible hybrid schedule as part of Fidelity’s dynamic working arrangement. This is a full-time regular opportunity. The work location for this role is 483 Bay Street in Toronto until approximately late 2026, when the work location will change to the new Mississauga office at 3 Robert Speck Parkway Who We Are At Fidelity, we’ve been helping Canadian investors build better financial futures for over 35 years. We offer individuals and institutions a range of trusted investment portfolios and services - and we’re constantly seeking to find new and better ways to help our clients. As a privately owned company, we boldly embrace innovation in all areas as we continue to grow our business into the future. Working with us means you’ll be part of a diverse and dedicated group of people who make a real difference for our clients and communities every day. You’ll have a wide range of opportunities to grow and develop your career in an inclusive environment where you’ll feel valued and supported to be your best - both personally and professionally. Fidelity Investments Canada is looking for a highly motivated and creative ‘Machine Learning Analyst’ to Fidelity Investments Canada is looking for a highly motivated and creative ‘Machine Learning Analyst’ to develop innovative AI/ML solutions to complex business challenges. Critical to the role’s success will be the individual’s penchant for continuous learning and a laser focus on delivering practical applications in a quickly evolving technical environment. As an ML Analyst, you will be collaborating with an interdisciplinary team that leverages large datasets using highly scalable computational resources to deliver end-to-end AI/ML based projects. You will be responsible for conducting exploratory data analysis, data pre-processing and transformation, developing ML algorithms, and assisting with deployment using both on-premise and cloud-based platforms. What You’ll Do As an ML Analyst, you will be collaborating with an interdisciplinary team that leverages large datasets using highly scalable computational resources to deliver end-to-end AI/ML based solutions. You will be responsible for conducting exploratory data analysis, data pre-processing and transformation, developing ML algorithms, and assisting with deployment using both on-premise and cloud-based platforms. Develop machine learning-based software solutions using open source and proprietary software systems. Conduct applied research to identify and understand different algorithms and methods for use case development. Collaborate effectively within agile scrum sessions alongside the Emerging Technology, IS ML Ops teams and business stakeholders to develop and implement high-impact business solutions. Rapid prototyping of new algorithms/approaches and conducting comparisons with existing algorithms and baselines. Iterate on model performance through error analysis, benchmarking, feature refinement, prompt evaluation, and comparison against baseline approaches. Assist the IS Infrastructure and IS ML Ops teams in designing customized ML environments as needed. Support projects through the documentation, monitoring and version control of models. Develop and evaluate Generative AI and Large Language Model solutions, including prompt engineering, retrieval-augmented generation, document intelligence, summarization, classification, and conversational AI use cases. Work with enterprise data platforms such as Snowflake to prepare, query, transform, and analyze structured and unstructured data for AI/ML and Generative AI use cases. Explore and prototype solutions using Snowflake Cortex and related cloud AI services where appropriate. What We’re Looking For A completed Master’s Degree in Computer Science, Statistics, Software Engineering or other STEM discipline, or equivalent working experience. Experience with data collection, data annotation, and active learning. Solid theoretical grounding in core machine learning concepts and techniques. 2+ years of experience within a data science, artificial intelligence and/or applied machine learning position. 1+ year of experience with cloud computing is an asset. 1+ year of experience building production machine learning models, and deploying them to solve inference challenges at scale is an asset. Strong understanding of machine learning approaches, including predictive modelling, supervised and unsupervised learning, NLP, Generative AI / Large Language Models, and model evaluation. AWS Certified Machine Learning and AWS Certified Data Analytics are assets. Investment Funds in Canada and/or Canadian Securities Course (CSI) is an asset. 1–2 years of experience working with Snowflake, including strong SQL skills, data transformation, query optimization, and familiarity with Snowflake Cortex or other native AI/ML capabilities. Expands the existing Snowflake requirement. Experience using Git for version control, including GitHub, branching, pull requests, and code reviews. Practical experience with Generative AI / Large Language Model workflows, such as prompt engineering, retrieval-augmented generation, embeddings, vector search, model evaluation, or orchestration frameworks such as LangChain, LlamaIndex, or similar tools. Familiarity with responsible AI practices, including model governance, privacy, explainability, hallucination mitigation, and secure handling of enterprise data. The Skills You Bring Strong communication skills and the ability to work with diverse stakeholders in a team environment. Ability to adapt quickly in the face of change using excellent problem-solving skills and creativity. Familiarity with popular Python-based AI/ML libraries, such as scikit-learn, PyTorch, pandas, NumPy, matplotlib, and associated workflows. Experience with deployment of machine learning model pipelines using AWS, such as SageMaker. Familiarity with containerization of ML models, including Docker and Kubernetes. Strong SQL skills for querying and transforming data across cloud data platforms and relational databases, including Snowflake and traditional platforms such as Oracle, SQL Server, DB2, or MySQL. Replaces “SQL skills for querying relational databases…” Demonstrated proficiency with deep learning, ensemble-based methods, NLP, time series analysis, and optimization techniques. Familiarity with LLM application development patterns, including prompt design, retrieval pipelines, embeddings, semantic search, and evaluation of generated outputs. Ability to translate business problems into practical AI/ML or Generative AI solutions, while balancing technical feasibility, business value, and risk considerations. Total Rewards That Reflect Your Impact We believe exceptional work deserves exceptional recognition. That’s why we offer a competitive compensation package designed to support your success today—and your financial well-being tomorrow. For this role, your total rewards include Base Salary and Discretionary Performance Bonus A competitive annual wage range of $105,000 to $129,000, based on your experience and qualifications. RRSP Contribution After 6 months of employment, we invest in your future with an RRSP contribution—no employee matching required. We’re proud to offer a compensation package that aligns with provincial pay transparency requirements. Some of the ways we’ll help you feel valued and supported as part of our team Flexible working arrangements - 100% remote, hybrid, and in office options Competitive total compensation, including company contributions to your group RRSP without a matching requirement from you Comprehensive health benefits that start on your first day, with 100% employer-paid premiums, that include up to $6000 annually for mental health services and therapy Parental leave top-up to 100% of your salar