Data Science Manager (CXD)
Leads the applied AI science function for Rakuten's conversational AI customer-service platform from Singapore, managing data scientists and ML engineers. Owns LLM lifecycle, RAG quality, evaluation/experimentation frameworks, memory/personalisation, driving model quality and business impact across Business Units.
- Compensation
- Not disclosed
- Location
- Singapore
- Work setup
- Onsite
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data science
- Posted
Skills
Must have
- 7+ years in applied machine learning or NLP
- 3+ years in a people management or technical lead role
- Deep hands-on experience with LLMs and RAG systems in production (retrieval pipeline design, reranking, chunking, knowledge base management)
- Ability to provide mathematical and algorithmic sign-off on system design decisions (orchestration, routing policies, retrieval scoring)
- Experience with agentic reasoning evaluation, multi-step orchestration analysis, and systematic model comparison frameworks
- Track record designing and running online experiments (A/B, interleaving, bandits) and translating results into product decisions
- Experience building error taxonomies, evaluation frameworks, and feedback loops that improve production quality metrics
- Experience building data science teams: hiring, performance management, career development
- Ability to synthesise analytical findings into product problem statements and influence prioritisation at leadership level
- Proficiency in Python
- Cloud ML infrastructure experience (GCP preferred)
- Familiarity with vector search and embedding models
- Strong communication skills with technical and non-technical senior stakeholders across cultures and time zones
- Comfortable working cross-regionally with key stakeholders based in Japan
Nice to have
- PhD in Computer Science, ML, NLP, or related discipline; equivalent research-level industry experience considered
Full description
Job Description:
Rakuten Group, Inc. is a global leader in internet services and has a diverse ecosystem spanning across e-commerce, fintech, communications and more serving approximately 1.8 billion members worldwide. Founded in Tokyo in 1997, the Group operates in over 30 countries and regions with more than 30,000 employees.
Based in Singapore's Central Business District, Rakuten Asia Pte. Ltd. serves as the regional headquarters for Asia, driving value through areas such as advertising product development, product strategy, and data management to support Rakuten Group's global ecosystem. Learn more at:
The Data Science Manager will lead the applied AI science function within Rakuten's CXD team, managing a team of data scientists and ML engineers responsible for the intelligence layer of our conversational AI platform. Based in Singapore and working closely with product and engineering counterparts across Japan and the wider APAC region, you will set the technical direction, build team capability, and drive measurable improvement across model quality, retrieval performance, and personalisation — ensuring that science directly moves business outcomes at scale.
This role is chartered around two foundational responsibilities that define the scientific integrity of the platform as it scales across multiple Business Units Runtime Decision Plane (Mathematical & Scientific Sign-off) and Learning Plane (Flywheel & Feedback Systems).Key Responsibilities
- Team Leadership & Science Strategy: Build, manage, and mentor a team of data scientists and ML engineers. Define the team's technical roadmap across both the Runtime Decision Plane and the Learning Plane, establish ways of working, and create an environment where rigorous science and rapid iteration coexist. Set the scientific bar for the group-wide Customer Service AI platform — enabling rapid, safe onboarding of distinct Business Units beyond the initial deployments. Partner closely with Product and Engineering leads across Singapore and Japan to align the science agenda with platform priorities and BU expansion commitments.
- LLM Strategy & Model Lifecycle Management: Own the end-to-end lifecycle of large language models in production — including vendor evaluation, model selection, fine-tuning, and version upgrade planning. Shift the team's focus from basic prompt optimisation to systematic model comparison frameworks and agentic reasoning evaluation, grounding every production decision in formal theory and empirical benchmarking rather than heuristic tuning. Provide scientific sign-off on orchestration strategies, multi-step agent routing policies, and chain-of-thought reasoning design — establishing a clear, defensible process for every model change that reaches production. Govern the cost-quality-latency trade-off across all BU deployments.