Manulife’s Group Functions AI team is scaling AI and advanced analytics capabilities across Finance, Treasury, Actuarial, and related enterprise functions to improve how decisions are made and how insights are generated. This role focuses on building solutions that use machine learning, GenAI, and modern analytical approaches to solve business problems at enterprise scale.
In this role, you will take business problem contexts and translate them into AI use cases such as predictive modeling, segmentation, anomaly detection, scenario analysis, and automation of analytical workflows. The emphasis is on building reusable, production-ready components that integrate into business workflows, with clear explainability, strong evaluation, ongoing monitoring, and governance-ready evidence.
Position Responsibilities: You will work closely with business stakeholders and engineering partners to deliver solutions that are explainable, robust, and operationally sustainable—helping accelerate decision cycles, improve consistency, and enable teams to focus on higher-value judgment where it matters.
1. Own end-to-end solution design for actuarial AI Translate business problems into a clear solution approach: business workflow, data flow, modeling approach, evaluation plan, and operational controls.
Apply strong design thinking: clarify user needs, define decision points, design for adoption, and make trade-offs explicit.
Create lightweight, high-quality design artifacts (e.g., system context, runtime sequence, agent/tool map where applicable, data lineage, decision log) that make build and governance straightforward.
Make smart design trade-offs: accuracy vs explainability, robustness vs speed, and model complexity vs operational sustainability.
2. Build strong ML, GenAI, and agentic capabilities for actuarial use cases Develop models such as predictive risk and behavior models, forecasting and scenario models, segmentation, anomaly detection, and optimization approaches.
Build GenAI capabilities such as retrieval-based solutions, structured summarization/extraction, and guided analytical workflows to accelerate insight generation.
Where applicable, design agentic workflows that coordinate multiple steps and tools (e.g., retrieval, calculations, rules, and structured outputs) while maintaining traceability and controls.
Engineer features from large structured and unstructured datasets and ensure solutions remain stable as data and assumptions evolve.
3. Set a high bar for evaluation and evidence Define performance expectations with stakeholders and implement out-of-time testing, backtesting, error analysis, stability checks, and sensitivity analysis.
For GenAI and agentic workflows, design practical evaluation: scenario coverage, edge cases, human review rubrics, quality scoring, and regression testing.
Document model limitations clearly and build guardrails that ensure outputs are used appropriately.
4. Partner closely to productionize and operate solutions Collaborate with data engineering, ML engineering, and software teams to productionize: pipelines, model packaging, CI/CD, deployment, and monitoring.
Implement monitoring for data quality, drift, performance deterioration, and operational failures; define remediation actions when thresholds breach.
Contribute to runbooks and support adoption and UAT with business users.
5. Work in a governed environment Produce documentation and evidence required for model risk review, including assumptions, validation results, monitoring plans, and UAT evidence.
Ensure privacy and security expectations are met through data minimization, appropriate access controls,



and safe handling of sensitive information.
6. Raise team capability Mentor junior scientists through design reviews, code reviews, and evaluation practices.
Help standardize how we build solutions using reusable templates, checklists, and examples to improve consistency and delivery speed.
Required Qualifications: 6–10 years of experience in applied data science, machine learning, or advanced analytics, with demonstrated end-to-end delivery into production beyond notebooks, including support for UAT and post-launch iteration.
Strong Python and SQL, with solid software engineering practices: Git-based workflows, code reviews, unit and integration testing, logging, readable code structure, and basic performance tuning.
Hands-on experience with modern DS/ML tooling such as scikit-learn, PyTorch or TensorFlow, and distributed processing platforms such as Spark or Databricks, including feature engineering and model development at scale.
Demonstrated ability to design and communicate solution architecture: produce clear diagrams and short specs covering data flow, runtime flow, interfaces, dependencies, failure modes, and operational controls; align stakeholders on trade-offs and scope.
Strong evaluation skills across ML and advanced analytics: backtesting or out-of-time testing, metric selection, error analysis, stability testing, and sensitivity analysis; ability to translate evaluation into business-ready acceptance criteria.
Experience building and operating monitored solutions: data quality checks, drift detection, performance deterioration monitoring, alerting, and practical remediation approaches.
Strong communication and stakeholder management: ability to explain outputs, limitations, uncertainty, and design decisions in plain language, and drive adoption in business workflows with domain partners.
Working knowledge of GenAI and agentic patterns, including when they add value and how to deploy them responsibly; experience contributing to at least one GenAI-enabled capability such as retrieval-based solutions, structured summarization/extraction, or tool-using workflows.
Preferred Qualifications: Experience delivering solutions in governed environments, including documentation, validation evidence, monitoring plans, UAT support, and approvals.
Experience with GenAI patterns such as retrieval-based solutions, structured outputs, tool/function calling, and agentic workflows, along with practical evaluation methods.
Familiarity with vector search and embeddings, semantic retrieval, and orchestration frameworks used to build production GenAI systems.
Experience implementing GenAI guardrails including accuracy controls, safe output formatting, data minimization, access controls, and human review workflows.
Ability to influence and mentor others through design reviews, code reviews, and evaluation practices without formal people management responsibility.
When you join our team: We’ll empower you to learn and grow the career you want.
We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
As part of our global team, we’ll support you in shaping the future you want to see.
#LI-Hybrid
The role being advertised is an existing vacancy.




マニュライフとジョン・ハンコックについて マニュライフ・ファイナンシャル・コーポレーションは、「あなたの未来に、わかりやすさを」を提供する、国際的な大手金融サービスプロバイダーです。当社について詳しくは、 https://www.manulife.co.jp/lをご覧ください。
マニュライフは機会均等を是とする雇用主です マニュライフ/ジョン・ハンコックでは、多様性を受け入れます。私たちは、サービス提供先であるお客さまと同様に、多様な人材を引きつけ、育成し、定着させ、文化や個人の力を受け入れる包括的な職場環境を促進するよう努めています。当社は公正な採用、定着、昇進、報酬に努めています。当社のすべての慣行およびプログラムは、人種、祖先、出身地、肌の色、民族的出自、市民権、宗教または宗教的信念、信条、性別（妊娠および妊娠関連の状態を含む）、性的指向、遺伝的特徴、退役軍人としての地位、性自認、性に関する表明、年齢、婚姻状況、家族状況、障害、または適用法で保護されるその他の要因に対する一切の差別を行うことなく管理されます。
雇用への平等なアクセスを提供するために、障壁を取り除くことが当社の優先事項です。人事担当者は、応募者が応募プロセス中に合理的配慮を要求する場合に協力します。配慮要求のプロセス中に共有されるすべての情報は、適用される法律およびマニュライフ/ジョン・ハンコックのポリシーに準拠した方法で保存および使用されます。申請プロセスにおいて合理的配慮を要求するには、[email protected]までご連絡をお願いします。
Referenced Salary Location Toronto, Ontario
Working Arrangement ハイブリッド勤務
Salary range is expected to be between $129,400.00 CAD - $179,400.00 CAD
Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance. The actual salary will vary depending on local market conditions, geography and relevant job-related factors such as knowledge, skills, qualifications, experience, and education/training. If you are applying for this role outside of the primary location, please contact [email protected] for the salary range for your location.
Manulife offers eligible employees a wide array of customizable perks, including health, dental, mental health, vision, short- and long-term disability, life and AD&D; insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans. We also offer eligible employees various retirement savings plans (including pension and a global share ownership plan with employer matching contributions) and financial education and counseling resources. Our generous paid time off program in Canada includes holidays, vacation, personal, and sick days, and we offer the full range of statutory leaves of absence. If you are applying for this role in the U.S., please contact [email protected] for more information about U.S.-specific paid time off provisions.
We use data and analytics technologies, such as artificial intelligence (AI), and automated processing tools, to analyze and process the information you provide to us or third parties in the application process. For more information, please refer to our personal information collection statement.

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📌 Senior Applied AI Engineer – GenAI Systems (Ontario)
🏢 Manulife Financial
📍 Ontario