# Data Science Manager (CXD) at Rakuten Asia

Rakuten Asia is hiring a Data Science Manager (CXD) in Singapore, Singapore. Level rates it Builds AI ●●●●; you can [apply on Rakuten Asia](https://jobsbylevel.com/go/2b12afc1-cf1c-4a04-a25c-58679e1a34c0?utm_medium=ai-agent).

AI Level 4, AI centrality 94 out of 100. Singapore.

## Details

- Company: [Rakuten Asia](https://jobsbylevel.com/companies/rakuten-asia)
- AI level: AI Level 4 (score 94 out of 100)
- Location: Singapore
- Posted: August 25, 2026
- Apply: https://jobsbylevel.com/go/2b12afc1-cf1c-4a04-a25c-58679e1a34c0?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-science-manager-cxd-at-rakuten-asia-986d79

## Prepare for this job

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## 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: https://global.rakuten.com/corp/ 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. RAG Architecture & Continuous Iteration: Lead the ongoing research and improvement of Retrieval-Augmented Generation systems, covering retrieval quality, reranking strategies, knowledge base health, and generation faithfulness. Drive a structured iteration cycle — hypothesis, offline evaluation, staged rollout, impact measurement — and maintain a clear improvement backlog prioritised against business impact. Distinguish retrieval gaps from LLM synthesis failures to ensure the evaluation framework correctly attributes root causes. Collaborate with Knowledge Engineering teams to ensure retrieval and content quality reinforce each other across all BU knowledge bases. Evaluation Framework, Experimentation & VoC Intelligence: Define and operate a multi-layer evaluation framework spanning offline benchmarks, online A/B experiments, and continuous production monitoring. Define and enforce pre-launch BU evaluation gates — statistical coverage thresholds, intent accuracy floors, hallucination rate ceilings — that every new BU deployment must pass before going live. Own the team's Voice of Customer analysis practice —

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