Machine Learning jobs in United States

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!

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

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Toronto, Ontario

Hybride

$129,400.00 CAD - $179,400.00 CAD

Si vous posez votre candidature à ce poste en dehors de la région principale, veuillez écrire à recruitment@manulife.com pour obtenir l’échelle salariale correspondant à votre région. Le salaire varie en fonction des conditions du marché local, de la géographie et de facteurs pertinents liés au poste telles les connaissances, les compétences, les qualifications, l’expérience et l’éducation ou la formation. Les employés ont également la possibilité de participer à des programmes de motivation et de toucher une rémunération incitative liée au rendement de l’entreprise et au rendement individuel.

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 recruitment@manulife.com pour obtenir de plus amples renseignements sur les dispositions relatives aux congés rémunérés spécifiques aux États-Unis.