Machine Learning Analyst

fil; Toronto; Hybrid; CAD 100,000 - 140,000; Full time

Job summary

Fidelity Investments Canada is seeking a highly motivated Machine Learning Analyst to develop innovative AI/ML solutions and deploy models at scale. You will collaborate with an interdisciplinary team, handling data analysis, feature engineering, and model deployment across on-premise and cloud platforms in a hybrid Toronto/Mississauga setting.

The role requires a Master’s in a STEM field and 2+ years in data science or ML, with strong Python, SQL, and Snowflake experience.

Qualifications

• Master’s degree in CS/Statistics/SE or equivalent.
• 2+ years in data science/AI or applied ML.
• 1+ year cloud computing experience is an asset.
• 1+ year building production ML models and deploying at scale.
• Strong ML foundation: predictive modelling, NLP, Generative AI, model eval.
• Snowflake SQL and data transformation experience.

Responsibilities

• Develop ML-based software solutions using open source and proprietary software systems.
• Conduct applied research to identify and understand algorithms for use case development.
• Collaborate with agile scrum teams to develop and implement high-impact business solutions.
• Rapid prototyping of new algorithms and comparisons with baselines.
• Iterate on model performance via error analysis, benchmarking, and feature refinement.
• Assist IS Infrastructure and IS ML Ops in designing customized ML environments.
• Support documentation, monitoring, and version control of models.
• Develop and evaluate Generative AI/LLM solutions including prompt engineering and retrieval-augmented generation.
• Work with enterprise data platforms such as Snowflake to prepare, query, transform, and analyze data for AI/ML use cases.
• Explore Snowflake Cortex and related cloud AI services.

Skills

Machine Learning

Python

SQL

Cloud computing (AWS)

Snowflake

Git

Generative AI

NLP

Education

Master's Degree in CS/Statistics/SE or equivalent

Tools

Snowflake Cortex

SageMaker

Docker

Kubernetes

LangChain

LlamaIndex

GitHub

Job description

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