1 day ago
Toronto, CanadaEntry Level / Mid Level
Responsibilities
- Translate Finance, Treasury, and Actuarial business problems into end-to-end GenAI and ML solution designs with documented workflows, data flows, evaluation plans, and operational controls.
- Build ML models for forecasting, classification, NLP, anomaly detection, optimization, and scenario analysis.
- Develop GenAI capabilities including RAG, structured summarization and extraction, transaction understanding, variance explanation, and tool-using workflows.
- Engineer features from structured and unstructured data and evaluate solutions using holdouts, backtesting, error analysis, scenario coverage, human review, quality scoring, and regression testing.
- Collaborate with Data Engineering, ML Engineering, and Software teams to package, deploy, monitor, and operate reliable production solutions.
- Write maintainable, tested code and support monitoring of data quality, drift, performance deterioration, and operational failures.
- Contribute to model-risk documentation, validation evidence, monitoring plans, UAT evidence, approvals, privacy controls, access controls, reproducibility, traceability, and auditability.
- Create reusable components, templates, examples, and testing patterns while supporting business-user adoption and UAT.
Requirements
- Master’s or PhD in Computer Science, Statistics, Machine Learning, Applied Mathematics, Operations Research, Engineering, or a related quantitative field.
- 0–3 years of applied data science or machine learning experience, including internships, co-ops, research, or early-career industry experience; strong academic project work may be considered.
- Strong Python and SQL skills with software engineering fundamentals including Git-based workflows, code reviews, unit and integration testing, logging, readable code, debugging, and basic performance tuning.
- Hands-on experience with data preparation, feature engineering, and model development using tools such as scikit-learn, PyTorch or TensorFlow, and Spark or Databricks.
- Ability to create structured technical approaches that define inputs, outputs, assumptions, failure modes, and evaluation methods.
- Exposure to building or evaluating GenAI solutions such as RAG, structured summarization or extraction, LLM-based classification, tool or function calling, or multi-step workflows.
- Strong ML and GenAI evaluation skills, including metric selection, holdout testing, error analysis, scenario coverage, edge-case analysis, and regression testing.
- Understanding of production-oriented development, including code packaging, APIs or services, configuration, output monitoring, and maintainability.
- Strong communication skills for explaining technical outputs, limitations, and design choices in plain language.
Benefits
- Hybrid working arrangement in Toronto, Ontario.
- Flexible environment supporting learning, career growth, well-being, and inclusion.
- Eligible employees may receive health, dental, mental health, vision, disability, life, AD&D, adoption/surrogacy, wellness, and employee/family assistance benefits.
- Retirement savings plans, including pension and a global share ownership plan with employer matching contributions, plus financial education and counseling resources.
- Paid holidays, vacation, personal and sick days, and statutory leaves of absence in Canada.
- Opportunity to participate in incentive programs tied to business and individual performance.
Tech Stack
Categories
About Manulife
Manulife is a Toronto‑headquartered public financial services company that provides life and health insurance, retirement plans, and wealth and asset management to individuals and institutions. It earns premiums and fee income from insurance, investment, and advisory products delivered across Canada, Asia, and Europe, and operates as John Hancock in the United States. Founded in 1887, the company is listed on the Toronto Stock Exchange.