Description and Requirements

Position Summary

The AVP, Data Science & AI , leads the delivery of data science and AI solutions for the Japan business and reports to the Japan CDO . The role is accountable for the full solution lifecycle, from problem definition and data sourcing through production deployment, monitoring, retraining or retirement, and measurement of realised business value.

This is a hands-on leadership role that combines strategic contribution, business partnership and technical depth. The role holder remains directly involved in solution design and code review, leads the adoption of modern data science, engineering and AI tooling, has first-line accountability for the quality of data used by the team, and leads the responsible use of AI in the solutions delivered.

Business requirements are often unclear at the outset: The role holder is expected to work with business stakeholders to define the decision or problem, agree scope and success measures, and build trust through transparent, consistent delivery.

Key Responsibilities

1. Data Science and AI Delivery

• Lead the end-to-end lifecycle of analytical and AI products, including problem framing, data sourcing, feature development, modelling, validation, deployment, monitoring, retraining and retirement.

• Build and prioritise the Japan use-case portfolio with the Japan CDO and business sponsors. Priority areas include underwriting triage, claims fraud and leakage detection, call center analytics and operational efficiency, agent and distribution productivity, customer acquisition and lead scoring, next best action, cross-sell.

• Establish and maintain technical standards for model development, validation, documentation and monitoring.

• Lead the application of generative AI and large language model solutions where appropriate, including Japanese-language document processing, agent and adviser support tools, call center knowledge assistants.

• Lead the selection and application of small language models (SLMs) for focused business use cases where privacy, latency, cost or deployment constraints make them appropriate, including task-specific evaluation, adaptation, retrieval, guardrails and production monitoring.

• Monitor deployed models for drift and performance degradation, and retrain or retire them as required.

• Maintain hands-on technical involvement, including design and code review, and explain and defend the team's models to Internal Audit, Risk and supervisory stakeholders.

2. Engineering and Productionisation

• Own the path from experiment to production, including deployment patterns, model registry, feature reuse, continuous integration and delivery, testing, observability and cost management.

• Promote disciplined, secure software engineering practices using Git-based collaboration and automation platforms, such as GitHub or comparable tools, including version control, pull requests, code review, testing and continuous integration and delivery.

• Own the curated data products, reusable features and analytics engineering discipline on which the team depends, ensuring consistency across models, reporting and agreed business definitions.

• Enable governed self-service and conversational analytics using fit-for-purpose capabilities, such as Databricks Genie or comparable tools, with trusted data, consistent metrics, business context and quality controls.

• Partner with the local and global platform teams on the underlying data platform, and contribute Japan requirements to the platform roadmap.

3. Data Governance and Data Quality

• Support on first-line data quality for all data used by the team, including quality rules embedded in pipelines, automated issue detection, root-cause analysis to source, and remediation agreed with system owners.

• Support on our data governance framework and assist our data stewards on lineage, metadata, retention and access.

4. Responsible Use of AI and Privacy

• Lead first-line implementation of the Responsible Use of AI framework in Japan, covering fairness and bias testing, decision-appropriate explainability, human oversight design, documentation and traceability.

• Apply heightened controls to customer-impacting decisions, including underwriting acceptance and rating, claims decisioning and fraud referral.

• Ensure compliance with the Act on the Protection of Personal Information, including requirements related to special care-required personal information, purpose limitation and cross-border transfer to regional and global headquarters.

5. Business Partnership and Global Alignment

• Act as the primary Data and Analytics counterpart for designated business functions.

• Translate unclear business requirements into scoped problems with a defined decision, success measure and accountable owner.

• Measure and report realised business value together with the business sponsor.

• Conduct workshops and training to strengthen business understanding of analytics and artificial intelligence.

• Adopt Enterprise frameworks, platforms and reusable assets where suitable, and represent Japan-specific requirements in regional and global forums.

6. Team Leadership

• Lead, develop and retain a team of data scientists, machine learning engineers, analytics engineers and analysts, together with external partners and vendors.

• Manage hiring, onboarding, performance and technical career development.

• Establish working practices including design review, code review and post-incident review.

• Manage team capacity across competing business demands.

• Maintain close and regular engagement with team members.

Required Qualifications

• Approximately 10 years or more of experience in data science, machine learning or advanced analytics, including at least 3 years leading technical teams.

• Demonstrated record of models deployed to production and in active business use.

• Experience with model governance, model risk management or responsible artificial intelligence in a supervised environment, including preparation of model documentation for independent review.

• Depth in machine learning and interpretability, supported by sound statistical foundations.

• Practical experience of model deployment and operation, including machine learning operations patterns, model registries, monitoring and retraining.

• Experience with modern collaborative development practices using GitHub or an equivalent platform, including source control, branching, pull requests, code review and CI/CD automation.

• Working knowledge of modern data and AI platforms, distributed processing, workflow orchestration, transformation frameworks and at least one major cloud platform.

• Applied production experience with language models, including SLMs and/or LLMs, covering use-case selection, evaluation, retrieval, guardrails, deployment and cost or performance management.

• Understanding of fairness metrics and explainability techniques, including their limitations.

• Experience working in a matrix structure between a local business and a regional or global head office.

• Business-level Japanese and English.

• Degree in a quantitative discipline such as computer science, statistics, mathematics, engineering, physics or economics.

Preferred Qualifications

• Experience in a regulated industry such as insurance, banking, asset management or healthcare.

• Production experience with generative AI in a regulated environment.

• Exposure to governed conversational analytics or AI-assisted business intelligence tools, such as Databricks Genie or comparable capabilities.

• Involvement in an Act on the Protection of Personal Information or equivalent privacy readiness programme.

• Exposure to actuarial modelling, pricing or Economic Solvency Ratio data.

• Experience managing vendors and systems integrators in the Japan market.

• Advanced degree or actuarial qualification.

Competencies

• Works effectively under unclear or evolving requirements.

• Builds trust with business stakeholders through delivery rather than escalation.

• Communicates effectively at both executive and technical levels.

• Raises model limitations and delivery risks openly and at an early stage.

• Develops the capability of team members.

Language Requirements

Language

Required level

Japanese

Business level

English

Business level

福利厚生

メットライフ生命では、ワークライフバランスと社員の心身の健康を促進する福利厚生を提供しています。社員はフレックスタイム制度、年次有給休暇、特別連続休暇、リフレッシュ休暇等の休暇を利用できます。また、社会保険や団体保険の他、旅行や英会話教室で使える割引も提供しています。柔軟に働けるよう、オフィス勤務と在宅勤務を組み合わせた勤務形態の導入や、短時間勤務制度、また柔軟性のあるドレスコードなども設けています。

Benefits We Offer

MetLife Japan offers a comprehensive benefits package that promotes work-life balance and employee wellbeing. Employees can take advantage of flex time policy and a generous time-off policy, national holidays, annual paid leave, special consecutive leave, and refreshment leave. We also provide full social insurance coverage, a commuting expense reimbursement, group insurance, and discounts on travel and English language lessons. To support work flexibility, employees also have hybrid work options, shortened working hours for parents with children in third grade or below, and a casual dress code.

メットライフについて

メットライフは、グローバルで展開する金融サービス企業です。個人および法人のお客さまがより確かな未来を築けるよう、保険、年金、社員福利厚生給付、資産管理のサポートを提供しています。1868年に創業したメットライフは、世界40以上の市場で事業を展開し、米国、アジア、中南米、ヨーロッパ、中東において業界をリードしています。

メットライフは、New Frontier戦略の一環として、AIを活用し、人を中心に据えた未来を築いています。私たちは、AIを活用して顧客へのサービス向上、地域社会への支援、そして働き方の変革を推進していく中で、好奇心、適応力、そして成長意欲を持つ人材を求めています。メットライフにおいて、AIは、人間の判断力、創造性、継続的な改善を支える責任あるパートナーであり、信頼、インクルージョン、そして長期的な価値の構築に貢献しています。

メットライフ生命は、日本初の外資系生命保険会社として、1973年2月に国内での営業を開始しました。当社のPurpose「ともに歩んでゆく。よりたしかな未来に向けて。」は、メットライフが誇るグローバルネットワークと世界中のベストプラクティスを活かし、お客さまのニーズに応え、地域社会との信頼関係を築くという信念を表しています。

About MetLife

MetLife Inc., through its subsidiaries and affiliates (MetLife), is one of the world's leading financial services companies, providing insurance, annuities, employee benefits and asset management to help individual and institutional customers build a more confident future. Founded in 1868, MetLife has operations in more than 40 markets globally and holds leading positions in the United States, Asia, Latin America, Europe and the Middle East.

As part of our New Frontier strategy, MetLife is building an AI-enabled, people-centered future. We're looking for people who bring curiosity, adaptability, and a growth mindset as we use AI to enhance how we serve customers, support communities, and evolve the way work gets done. At MetLife, AI is a responsible partner that supports human judgment, creativity, and continuous improvement while helping us build trust, inclusion, and long-term value.

MetLife Japan began operations in February 1973 as Japan's first foreign-owned life insurance company. Our purpose, "Always with you, building a more confident future," encapsulates our strong commitment to leveraging our global network and best practices worldwide to stand with our customers and build trust with our communities.