ABOUT THE JOB AND YOU
JOB TITLE Data Science Lead
REPORTING TO Head of Digital Transformation
LOCATION Shanghai, China
MAIN PURPOSE
The Data Science Lead anchors the Data Science strategy and delivery of APAC’s Digital Transformation agenda. This role leads the Digital Science squad (Data Architects & Data Scientists) and works closely with ITD and CBU Business Owners to drive advanced data science exploration, build scalable data foundations, and ensure strong, business‑relevant data governance.
The role is accountable for delivering high‑quality Data Science solutions and PoCs that prove value (0→1) on APAC Digital Priorities and prepare successful handover for scale to ITD.
The role combines strong Data Science and mathematical expertise with a consulting mindset — shaping APAC’s data and modelling strategy, guiding PMOs, and ensuring that high‑priority initiatives (e.g. Demand Forecast, MMM) are delivered with rigor, clarity, and measurable business impact.
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KEY RESPONSIBILITIES
Data Science Strategy & Data Governance Leadership (30%)
Define and drive the APAC Data Science strategy, including modelling principles, analytical standards, governance guardrails, and KPIs to ensure business impact.
Act as the APAC Data Science Centre of Excellence, co-work with ITD to build internal AI Capability, setting the bar on modelling quality and analytical rigour.
Ensure strong data quality, privacy, and governance as foundations for reliable modelling, experimentation, and insight generation.
Drive alignment with ITD on data architecture, pipelines, integration, and security to effectively support Data Science use cases.
Serve as the technical Data Science leader on high‑priority initiatives, including readiness assessments, key modelling decisions, and the ability to challenge external vendors and partners.
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Technical Direction on Priority Initiatives (40%)
Lead and prioritise Data Science exploration and PoCs for key APAC digital priorities, focusing on high‑impact use cases, including but not limited to:
Demand Forecast (ML‑based forecasting)
Lead end‑to‑end Data Science work, including modelling, validation, UI/UX, assetisation, and guidebook creation to support scale.
Marketing Mix Modelling (MMM)
Ensure strong modelling foundations and an appropriate data governance strategy to enable long‑term, sustainable implementation.
Act as a technical reference to challenge vendors and partners on modelling choices and assumptions.
Agentic AI
Drive the technical implementation of cutting‑edge Agentic AI solutions, in close collaboration with external partners and ITD.
Leverage modern AI coding tools to accelerate development, experimentation, and productivity.
Responsibilities include:
Framing business problems into clear modelling and solution designs (0→1).
Assessing data and modelling readiness before initiatives move to scale.
Supporting PMOs with technical clarity, risk identification, and vendor assessment.
Reviewing PoC outcomes to ensure rigour, clarity, and proven business value.
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Assetisation & Cross CBU Scaling (15%)
Work with ITD to translate successful PoCs into reusable Data Science assets (models, features, playbooks).
Codify key learnings from PoCs to enable efficient, consistent, and repeatable scaling across CBUs.
Define and lead a consistent PoC approach, from data foundations, to modelling layer, to business layer with strong UI/UX.
Ensure analytical consistency across CBUs, avoiding fragmented modelling and interpretation.
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Leadership & Team Development (15%)
Lead and coach the Data Architect and Data Scientist, ensuring clarity on priorities, modelling quality, and professional development.
Guide the team on Data Science methodologies, modelling approaches, and analytical best practices.
Foster a culture of curiosity, continuous learning, and disciplined exploration, including benchmarking emerging AI technologies.
Build a high‑performing, consulting‑oriented team combining strategic thinking and hands‑on delivery.
QUALIFICATIONS
Education
University degree in Computer Science, Data Engineering, Statistics or related technical field.
A postgraduate degree (Master / PhD) is a strong plus.
Experience
7+ years of experience in Data Science, Applied Machine Learning, or Advanced Analytics.
Proven track record leading Data Science exploration, PoCs, and value proof (0→1) in complex business environments.
Experience acting as a technical reference, including the ability to challenge external vendors and partners on modelling choices and assumptions.
Background in consulting or cross‑functional leadership roles is a strong plus.
Technical Expertise
Strong hands‑on experience with Python, Data Science modelling, and experimentation, supported by a solid statistical and mathematical foundation.
Proven ability to use AI coding tools (e.g. GenAI‑assisted development, agentic coding frameworks, copilots) to improve development speed, iteration, and overall efficiency.
Good working knowledge of modern analytics and AI platforms such as Databricks and AliCloud, sufficient to design, test, and validate Data Science solutions in enterprise environments.
Strong understanding of the end‑to‑end Data Science lifecycle, from exploration and PoC to preparation for scale.
Ability to design robust, reusable modelling assets that translate into business‑impactful insights.
Interpersonal Skills
Strong strategic thinking: ability to structure problems, simplify complexity, and influence senior stakeholders.
Excellent communication skills: able to explain technical concepts to nontechnical audiences.
Strong curiosity, ownership, proactive mindset, and high reliability.
Comfortable navigating ambiguity and working in a fast paced, multicultural environment.
