Manulife’s Group Functions AI team is scaling AI and advanced analytics capabilities for Actuarial partners to improve how decisions are made and how insights are generated! This role sits within the AI team and focuses on building solutions that use machine learning, optimization, and modern analytical approaches to solve actuarial-adjacent problems at enterprise scale.

In this role, you will take actuarial problems and translate them into AI use cases. These include predictive risk and behavior modeling, grouping, outlier identification, scenario and sensitivity engines, and automation of controls and analytical routines across recurring cycles. The emphasis is on building reusable, production-ready components and analytical products that integrate into business workflows, with clear explainability, strong evaluation, ongoing monitoring, and governance-ready evidence!

Position Responsibilities:
You will work closely with actuarial collaborators and engineering partners. Together, you will deliver solutions that are explainable, robust, and operationally balanced. These solutions help accelerate decision cycles, improve consistency, and let teams focus on higher-value judgment where it matters.

Own end-to-end solution design for actuarial AI

• Translate actuarial 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.

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.

Set a high bar for evaluation and evidence

• Define performance expectations with collaborators 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.

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.

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.

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 build and communicate solution architecture by producing clear diagrams and short specs. These cover data flow, runtime flow, interfaces, dependencies, failure modes, and operational controls. Align collaborators 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 collaborator management: ability to explain outputs, limitations, uncertainty, and build decisions in plain language, and drive adoption in business workflows with domain partners.
• Actuarial domain depth demonstrated through significant experience partnering with actuarial teams or solving actuarial-context problems, with comfort in working with actuarial constraints, reconciliation expectations, and governed decision processes.
• Working knowledge of GenAI and agentic patterns includes understanding when they add customer value. You should also know how to deploy them responsibly. Experience contributing to a GenAI-enabled capability like retrieval-based solutions, structured summarization/extraction, or tool-using workflows is required.

Preferred or Nice to have:

• Actuarial background through education, credentials including ASA or FSA or progress toward them, or substantial experience working in actuarial teams and workflows.
• 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

Le poste annoncé correspond à une vacance existante.

À propos de Manuvie et de John Hancock

La Société Financière Manuvie est un chef de file mondial des services financiers qui aide les gens à prendre leurs décisions plus facilement et à vivre mieux. Pour en apprendre plus à notre sujet, rendez vous à l’adresse www.manuvie.com.

Manuvie est un employeur qui souscrit au principe de l’égalité d’accès à l’emploi

Chez Manulife/John Hancock nous valorisons notre diversité. Nous nous efforçons d’attirer, de perfectionner et de maintenir une main d'oeuvre qui est aussi diversifiée que nos clients, et de favoriser la création d’un milieu de travail inclusif qui met à profit la diversité de nos employés et les compétences de chacun. Nous nous engageons à assurer un recrutement, une fidélisation, une promotion et une rémunération équitables, et nous administrons toutes nos pratiques et tous nos programmes sans discrimination en raison de la race, de l’ascendance, du lieu d’origine, de la couleur, de l’origine ethnique, de la citoyenneté, de la religion ou des croyances ou des convictions religieuses, du genre (y compris grossesse et affection liée à une grossesse), de l’orientation sexuelle, des caractéristiques génétiques, du statut d’ancien combattant, de l’identité de genre, de l’expression de genre, de l’âge, de l’état matrimonial, de la situation de famille, d’une invalidité ou de tout autre motif protégé par la loi applicable.

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Manuvie offre aux employés admissibles une vaste gamme d’avantages sociaux personnalisables, notamment une assurance soins médicaux, soins dentaires, santé mentale, soins de la vue, invalidité de courte et de longue durée, assurance vie et assurance DMA, assurance adoption, de maternité de substitution et de soins médicaux non urgents ainsi que des programmes d’aide aux employés et leur famille. Nous proposons également aux employés admissibles différents régimes d’épargne-retraite (y compris des régimes de rente et un programme international d’actionnariat assortie de cotisations patronales de contrepartie) ainsi que des ressources en matière d’éducation et de conseils financiers. Notre généreux programme de congés rémunérés au Canada comprend les jours fériés, les congés annuels, les congés personnels et les congés de maladie, et nous offrons toute la gamme des congés autorisés prévus par la loi. Si vous posez votre candidature à ce poste aux États-Unis, veuillez écrire à hr@manulife.com pour obtenir de plus amples renseignements sur les dispositions relatives aux congés rémunérés spécifiques aux États-Unis.

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