Technology Business Partner, China IT R&D

About the role
The Technology Business Partner is the trusted technology advisor to our R&D China, includes Clinical Operations, Bioinformatics and Data Science, Regulatory Affairs and Laboratory teams. You turn their scientific and operational goals into a technology and AI roadmap, then lead a product and project management team to deliver it. The role and every team it works with are based in China, so solutions are designed for China's regulatory, cloud and AI environment from the start.
Life sciences IT is moving from running projects to owning products, and from experimenting with AI to running it in regulated work. This role sits at the centre of that shift. You are expected to sit with a function head to shape priorities in the morning and debate architecture trade-offs with engineers in the afternoon, and to be credible in both rooms.
The role suits someone who works well without a detailed brief. Many of the problems are new, the regulatory expectations for AI are still forming, and you will often need to define the question before answering it.
Key responsibilities
Strategic business partnering

- Act as the single point of accountability for technology in your business domains, and as a trusted advisor to their leadership teams.

- Understand each function's objectives, processes and pain points well enough to challenge requests and propose better options.

- Influence senior stakeholders on priorities, investment and trade-offs, including saying no with a clear rationale.

- Represent your domains in IT leadership forums and represent IT in business leadership forums.
IT strategic planning

- Own a rolling 2 to 3 year technology roadmap for each domain, aligned with business strategy and enterprise architecture.

- Build business cases with clear value, total cost of ownership and risk, and track realised benefits after go-live.

- Manage the domain portfolio and budget, including demand intake, prioritisation and capacity planning.

- Drive build, buy or partner decisions and manage key vendor and SaaS relationships.
Product ownership and delivery leadership

- Lead, coach and grow a team of product and project managers who deliver outcomes, not only milestones.

- Run your domains as product lines with a vision, backlog, user feedback loop and adoption metrics.

- Hold the team accountable for scope, schedule, cost, quality and change management.

- Work in agile and hybrid delivery models, adapting to the validation needs of regulated systems.
AI and GenAI enablement

- Identify and prioritise AI use cases by business value, feasibility, data readiness and risk.

- Take use cases from proof of concept to production, with clear success measures and an exit rule for pilots that do not deliver.

- Explain to business leaders in plain language what AI can and cannot do, including LLMs, retrieval-augmented generation, agents, fine-tuning and evaluation.

- Apply responsible AI practice: human oversight, model evaluation and monitoring, data privacy, and fitness for regulated use.

- Build AI literacy and adoption in the business functions you support.
Solution and architecture engagement

- Discuss solution design with engineers and architects as a peer: integration patterns, APIs, cloud, data platforms, security and scalability.

- Make sure solutions fit the enterprise architecture, reuse shared platforms and reduce technical debt. Design for AWS China regions and Alibaba Cloud, and for the AI models and services available in China.

- Champion data as a product, with FAIR principles and clear ownership, so that clinical, lab and omics data can be reused for analytics and AI.
Governance, compliance and risk

- Ensure systems in scope meet GxP, data integrity, privacy and security requirements, working with QA and IT security. This includes China requirements on personal information, human genetic resources, data localisation and cross-border data transfer.

- Apply risk-based validation so that compliance effort is proportionate to patient and data risk.

- Monitor regulatory and industry developments on AI and digital in life sciences, and adjust the roadmap accordingly.
China data and cybersecurity compliance

- Make every system in scope compliant by design with China's cybersecurity, data security and personal information laws, working with legal, QA and IT security.

- Ensure systems are graded, filed and assessed under the Multi-Level Protection Scheme (MLPS 2.0), and that findings are closed.

- Maintain a data inventory and classification for the four domains, covering important data, sensitive personal information and human genetic resources data.

- Keep regulated data hosted in mainland China by default. Assess any cross-border transfer, overseas vendor or remote access, and complete the required mechanism before data moves.

- Ensure human genetic resources approvals and filings cover how clinical, sample and omics data is stored, analysed and shared in systems.

- Run personal information protection impact assessments for new systems and AI use cases, and carry out data-processing due diligence on vendors.

- Ensure AI solutions use models and services that meet China's generative AI requirements, and that confidential or regulated data does not reach unapproved AI services.
Domain scope
The role covers four business domains, each with its own system landscape and AI opportunities.
Domain
Typical systems and platforms
Where the industry is heading
Clinical Operations
CTMS, EDC, eTMF, eCOA, RTSM, risk-based quality management
AI-assisted protocol design and feasibility, site selection and enrolment forecasting, automated TMF document handling, decentralised and hybrid trial models
Bioinformatics and Data Science
Cloud and HPC compute, workflow pipelines, data lakehouse, MLOps and LLMOps tooling
Biological foundation models, agent-assisted analysis, reproducible pipelines, governed self-service data and compute with cost control
Regulatory Affairs
Regulatory information management, submission publishing, document management, regulatory intelligence
GenAI-assisted authoring of submission documents, faster responses to health authority queries, structured content and data-driven submissions
Laboratory
LIMS, ELN, scientific data management, instrument integration, lab automation
Connected and automated labs, instrument data captured at source, AI-ready experimental data, digital quality control
Required qualifications
Experience and education

- Bachelor's degree or above in a STEM discipline, such as computer science, engineering, data science or life sciences.

- 10+ years in IT or digital roles, including 3+ years in business partnering, product management or solution delivery.

- Experience in pharma, biotech, CRO, diagnostics or medical devices, with exposure to at least two of the four domains in scope.

- 3+ years leading product or project managers, with a record of delivering multi-system programmes.
Product mindset

- Thinks in outcomes, users and adoption, not only in requirements and go-live dates.

- Has managed a product roadmap and backlog, and has used data and user feedback to reprioritise.

- Can define value measures for a product and report on them after launch.
AI and GenAI

- Solid working understanding of how machine learning and large language models work, including their limits, failure modes and cost drivers.

- Hands-on involvement in at least one AI or GenAI solution taken from idea to production use.

- Understands data readiness, model evaluation, human-in-the-loop design and the governance needed in a regulated setting.
Software architecture

- Solid understanding of modern architecture: cloud and SaaS, APIs and integration, data platforms, identity and security.

- Able to read and challenge a solution design, and to explain trade-offs to non-technical leaders.

- Familiar with the software development lifecycle, DevOps practices and validated system delivery.
Stakeholder influence and strategic planning

- Proven ability to influence senior business stakeholders without formal authority.

- Experience building multi-year IT roadmaps, business cases and budgets.

- Strong vendor management and negotiation experience.

- Clear written and spoken communication in Mandarin and English, for executive and technical audiences.
Regulated environment

- Working knowledge of GxP, computerised system validation or assurance, 21 CFR Part 11 and EU Annex 11, data integrity and data privacy requirements.
China context

- Experience delivering IT or digital solutions for life sciences organisations in China.

- Working knowledge of NMPA expectations for clinical trials and submissions, and of China GCP.

- Familiarity with China's AI ecosystem, including locally available large language models and model services.
AWS and Alibaba Cloud

- Practical knowledge of both AWS and Alibaba Cloud: core compute, storage, database, networking, security and AI services.

- Understands how AWS China regions differ from AWS global regions in operation, accounts and service availability.

- Able to compare the two clouds for a given workload and explain the choice on capability, compliance, cost and skills.

- Experience with cloud governance across more than one provider: identity and access, landing zones, security baselines and cost management.

- Experience running data, analytics or AI workloads on at least one of the two clouds, including compute-heavy scientific workloads.
China data and cybersecurity requirements
Working knowledge of the following, and experience applying them to system design and data flows:

- Cybersecurity Law, Data Security Law, Personal Information Protection Law and the Network Data Security Management Regulations.

- Multi-Level Protection Scheme (MLPS 2.0): system grading, filing, assessment and remediation.

- Data classification and grading, including identification of important data and of sensitive personal information such as health and genetic data.

- Cross-border data transfer mechanisms: security assessment, standard contract and certification.

- Human genetic resources regulations covering collection, storage, use and external provision of samples and data.

- Personal information protection impact assessments, consent management and compliance audits.

- National standards for health and medical data security.

- Commercial cryptography requirements for information systems.

- Generative AI rules, including service management measures, algorithm and model filing, and labelling of AI-generated content.
Preferred qualifications

- Master's degree or PhD in a STEM discipline.

- Earlier career experience as a software engineer, solution architect, data scientist or bioinformatician.

- Experience with clinical platforms, regulatory information management, LIMS or ELN implementations.

- Familiarity with clinical and regulatory data standards such as CDISC, eCTD and IDMP.

- Knowledge of current AI regulation and guidance relevant to life sciences, such as China's generative AI service rules, NMPA and other health authority guidance on AI, the EU AI Act and ICH E6(R3).

- Experience with cloud cost management for compute-heavy scientific workloads.
Personal attributes

- Self-motivated. Sets own direction, finds the important problems and moves on them without waiting to be asked.

- Comfortable with ambiguity. Brings structure to unclear situations and makes sound decisions with incomplete information.

- Trusted advisor. Builds credibility through honesty, follow-through and an independent point of view.

- Bilingual in business and technology. Translates in both directions without losing meaning.

- Curious and fast-learning. Keeps up with a fast-moving AI field and with the science of the functions supported.

- People leader. Develops the team, delegates well and holds a high bar for delivery.

- Pragmatic. Balances speed, value, compliance and technical quality.
First 30, 60 and 90 days
The first 90 days decide whether the business sees this person as a trusted advisor, so each 30-day stage has defined actions and outputs.
Days 1 to 30: Listen and learn
Understand the business, the people and the landscape before proposing change.

- Meet the head and key users of each of the four domains, and record each function's top three goals and top three pain points.

- Meet the engineering, architecture, data platform, QA and IT security leads to learn how delivery works today.

- Hold one-to-ones with every PM on the team, and review each in-flight project for status, risk and expected value.

- Review the system landscape, vendor contracts and budget for the four domains.

- Check compliance status of key systems: validation, personal information protection, human genetic resources and data hosting.

- Take stock of AI pilots and AI tools already in use, including unofficial ones.
Output by day 30: a stakeholder map, a baseline of the system landscape and project portfolio, and a written summary of first observations shared with the line manager.
Days 31 to 60: Diagnose and align
Turn observations into agreed priorities and show early momentum.

- Play findings back to each function head and agree the top priorities for their domain.

- Identify two or three quick wins and start work on them.

- Shortlist AI use cases, scored on business value, feasibility, data readiness and compliance risk.

- Assess the PM team's skills and ways of working, and agree a common product and delivery model with clear roles.

- Set up a regular governance rhythm with each domain for demand intake and progress review.

- Escalate urgent risks, such as compliance gaps, troubled projects or contract renewals, with a recommended action.
Output by day 60: an agreed priority list per domain, an AI use case shortlist, a team operating model, and at least one quick win in progress.
Days 61 to 90: Plan and start delivering
Commit to a roadmap and deliver the first visible results.

- Present a 12 to 18 month technology and AI roadmap for each domain, with investment needs, and gain endorsement from function heads and IT leadership.

- Launch the first one or two AI use cases, each with an owner, success measures and a timeline.

- Deliver at least one quick win that business users can see and use.

- Put portfolio reporting in place, covering delivery, adoption, business value and stakeholder satisfaction.

- Agree personal and team objectives for the rest of the year with the line manager.
Output by day 90: an endorsed roadmap, the first AI use case under way, one quick win delivered, and regular portfolio reporting running.
Beyond 90 days

- By 6 months: the PM team works to one consistent product and delivery model, and the first AI use case has measured results.

- By 12 months: at least [2] AI or digital solutions are in production with measured business value, the portfolio is delivered to plan, and business leaders describe IT as a strategic partner.

Date Posted
12-11月-2026
Closing Date
30-12月-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.