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.

Machine Learning Analyst

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