Introduction to role Are you ready to turn complex cross-border data rules into practical pathways that unlock AI-driven drug discovery? Do you thrive at the intersection of scientific data, regulatory interpretation, and operational execution where your decisions enable researchers to move faster with confidence? About the Beijing AI Center
The Beijing AI Center is a new strategic investment by AstraZeneca to accelerate drug discovery through AI. The center brings together AI researchers, computational scientists, and platform engineers to apply foundation models, agentic AI, and large-scale scientific computing to real R&D problems. Situated in one of the world’s most dynamic AI talent markets, it
operates
at the intersection of
biologics
discovery, computational chemistry, and AI-driven drug discovery.
The center is structured around three pillars: Discovery verticals (therapeutic design and preclinical predictions), Data & AI Platforms, and Ecosystem Partnerships with leading Chinese academic institutions and AI companies.
Accountabilities:
This role is the transfer-readiness owner for enabling cross-border scientific data access into China for the Beijing AI Center.
It addresses the operational gap that exists before formal approval:
the
preparation
work needed to ensure incoming data requests are assessed against sending-jurisdiction restrictions, appropriately scoped, decision-ready, and auditable.
The primary data flow is global AstraZeneca R&D into China.
The Beijing AI Center needs access to molecular libraries, assay data, omics reference sets, compound data, clinical datasets, and other scientific assets from AstraZeneca's global portfolio to power AI-driven drug discovery. The regulatory challenge sits largely on the sending side — determining what can be transferred or accessed given
restrictions ( e.g.,
D OJ
EO
1 411 7 )
on bulk sensitive personal data, export control considerations, and AstraZeneca's internal data governance policies — as well as ensuring appropriate classification and protection once data arrives in China.
This is not a final approver role. Rather, it is a dedicated role focused on first-pass triage, provenance tracing, metadata review, annotation coordination, evidence package preparation, and process tracking. The role is intended to reduce the fragmented burden currently carried in an ad hoc way by scientists, business teams, and SMEs, especially for China-related or otherwise non-standard requests.
Information from public records, not immigration advice.
The salary is an estimate from national earnings data, not the employer's figure.
Why
In favour: Licensed for Skilled Worker
AstraZeneca UK Limited is on the register with an active Skilled Worker route (Worker (A rating)).
Home Office register of licensed sponsors
In favour: Occupation is eligible for Skilled Worker
Occupation code 2439 (Business, research and administrative professionals n.e.c.) is eligible for the Skilled Worker route on the rules in force from 11 Nov 2025.
Immigration Rules Appendix Skilled Occupations
Against: Estimated salary is below the going rate
No salary is stated, so this uses an estimate of £44,939 from ASHE estimate (low end of the no seniority marker band) (1 Apr 2025). It falls £11,661 short of the going rate for occupation code 2439 of £56,600. Because that salary is estimated rather than stated, this rule counts half.
Immigration Rules Appendix Skilled Occupations
In favour: A rating on the licence
The register shows “Worker (A rating)”, the rating a licence carries when it is not on an action plan.
Home Office register of licensed sponsors
In favour: A larger employer
Companies House records accounts of category “FULL”, filed by companies above the small-company thresholds.
Companies House
Not established: The advert says nothing about sponsorship
We searched the advert for the usual wording in both directions and found none of it.
The advert itself
Home Office register of licensed sponsors snapshot
Information from public records, not immigration advice.
The immediate priority is China data transfer, but the role should be designed for broader global cross-border sharing.
The
role will help stabilize the current interim process while building a more scalable and consistent operating model that can
inform
a future integrated solution.
This role sits at the intersection of scientific data, compliance preparation, and operational execution. It requires enough breadth across R&D data domains
and cross border transfer regulations
to work effectively across biologics, small molecules, and safety-related contexts, while also knowing when to pull in domain SMEs, the R&D Data Office,
Privacy, Compliance,
and designated approvers.
What You Will Do
Transfer Readiness & First-Pass Triage
Design and own the intake process
for cross-border data sharing requests — including request workflow, queue management, prioritization criteria, and routing logic
Own first-pass intake triage for cross-border data sharing requests, with mandatory review for China-related or non-standard requests and simplified routing for straightforward requests
Check request completeness including business justification, recipient, intended use, data source, format, and baseline supporting information
Provide first-pass triage recommendation on whether a request is likely in scope, out of scope, or ambiguous, while preparing the supporting rationale for formal review
Escalate ambiguous or higher-risk cases to the R&D Data Office, SMEs, and designated approvers with clear questions and structured evidence
Manage DOJ compliance assessment for CRO ordering
evaluating data shared with China-based CROs against EO 14117 thresholds and transaction type classifications
Lead provenance tracing for legacy or incompletely documented datasets by reviewing records, systems, and source history across relevant platforms
Coordinate metadata collection and completion needed to support transfer assessment and downstream usability
Support annotation and evidence preparation so datasets are sufficiently described, contextualized, and auditable before review
Partner with bioinformatics and scientific SMEs to resolve data history, source, ownership context, and metadata gaps
Differentiate between new / well-annotated data and legacy / incomplete data, applying a lighter path for the former and a deeper tracing effort for the latter
Data Curation & China-Readiness
Curate and annotate scientific data assets
(e.g., structure data in GDB) to ensure datasets are complete, well-structured, and usable for Beijing AI Center workstreams
Perform structure data curation
supporting both Biologics Engineering and Small Molecules domains
Apply
appropriate ontologies , standardized formats, and metadata tagging
to support cross-domain reuse and AI/ML consumption
Partner with domain scientists
to resolve data quality, completeness, and annotation gaps prior to transfer
Workflow Management & Process Improvement
Prepare structured evidence packages for Data Owners, Data Stewards, and designated approvers so requests are decision-ready before formal review
Track the interim data sharing process from intake through approval, transfer execution, validation, and closure
Act as process coordinator / tracker during transfer execution, following up on dependencies and ensuring documentation is complete, without taking over technical execution tasks
Capture recurring issues, requirements, and control points from the interim process to support the design of a future integrated data sharing solution
Improve templates, checklists, trackers, SOP inputs, and audit-readiness practices over time
Cross-Functional Partnership & Training
Work with the R&D Data Office to
identify
what data is likely in scope or out of scope across different workstreams
Partner with Privacy and Compliance
to ensure transfer assessments reflect current privacy requirements and that escalation pathways are well-defined
Build practical understanding of the systems used across biologics, small molecules, and safety and how data is stored, annotated, accessed, and extracted from separate systems
Provide guidance to business and scientific teams on transfer readiness expectations,
required
metadata, and supporting information
Reduce ad hoc burden on scientists and business teams by becoming the clear owner of the manual preparation work needed before approval
Essential Skills/Experience:
Education:
BSc/ MSc, or equivalent advanced training in life sciences, bioinformatics, data science, information management, or a related field preferred
Years:
Significant experience
in pharmaceutical or biotech R&D data environments, typically
4 + years post-qualification
Domain breadth:
Experience spanning multiple stages of drug discovery and development and/or multiple scientific data domains (e.g., discovery, preclinical, clinical, post-market)
Core
expertise :
Practical experience in one or more of: scientific data management, data stewardship, data governance, research data operations, data privacy operations, or regulated data workflows
Cross-border data work:
Experience assessing, preparing, or supporting cross-border data sharing decisions — particularly understanding sending-side regulatory requirements and how to structure compliant access
Governance preparation:
Experience preparing information for governance, compliance, or approval decisions — not only executing downstream data processing
Skills
Regulatory literacy:
Ability to understand and operationalize requirements from multiple regulatory frameworks (US DOJ, internal policy) at a working level — sufficient to perform first-pass assessment and know when to escalate
to
Legal
DOJ EO 14117 familiarity:
Working understanding of the DOJ's bulk sensitive personal data framework — including threshold categories, covered transaction types, prohibited vs. restricted classifications, and exemption pathways — is strongly preferred
Domain-specific data skills:
Familiarity with structural biology/chemistry data, molecular data formats, and scientific annotation standards (e.g., structure-activity relationships, compound descriptors, GDB or equivalent platforms)
Domain versatility:
Ability to work across biologics, small molecules, clinical, omics, imaging, and safety-related data contexts with enough breadth to assess data content and origin
Analytical rigor:
Ability to assess data volumes against thresholds, map data to
jurisdictions , evaluate exemption applicability, and structure clear decision-support documentation
Operational discipline:
Strong documentation rigor, process discipline, and audit mindset
Stakeholder management:
Credibility across scientists, AI researchers, SMEs, legal/privacy teams, and governance approvers — able to translate between technical data needs and compliance requirements
Problem-solving:
Ability to
identify
alternative access patterns, minimization strategies, and creative solutions when straightforward transfer is restricted
Systems literacy:
Enough familiarity with data platforms to understand how data is stored, what metadata exists, and how to assess data content without requiring deep technical data engineering skills
China / Cross-Border Context
China data classification : Familiarity with China's DSL data classification framework and what obligations apply to data once it enters China is preferred
Global matrixed environment:
Ability to work effectively across China and non-China stakeholders — coordinating with US, EU/UK, and global teams on data access requests
Mindset
Comfortable with ambiguity:
Regulatory frameworks are evolving (particularly DOJ implementation guidance); this role will often work with emerging interpretations, grey areas, and imperfect precedent
Low ego, high impact:
Willing to do detailed threshold calculations,
jurisdiction
mapping, and documentation work that enables better decisions at scale
Enablement-oriented:
Approaches compliance as a problem to solve, not a gate to close — actively seeks minimization strategies, alternative access patterns, and creative compliant solutions to get researchers what they need
Structured and practical:
Able to turn regulatory requirements and fragmented information into a clear, repeatable operating process
Collaborative and credible:
Able to work across scientific, AI, legal, privacy, and governance stakeholders with equal fluency
Judicious and disciplined : Understands the difference between first-pass triage and final approval accountability
Time zone flexibility:
Comfortable working across US/UK and China hours as needed to support Beijing AI Center stakeholders
Desirable Skills/Experience:
Education: PhD within relevant field
Experience in both discovery-stage and later-stage R&D data environments
Exposure to biologics, omics, sequence data, compound data, imaging data, AI/ML training datasets, or safety/pharmacovigilance data
Direct experience operationalizing DOJ EO 14117 or similar US national security data restriction frameworks
Experience with privacy-enhancing technologies (pseudonymization, tokenization, synthetic data, federated learning, secure computation) as transfer alternatives
Experience with secure research environments, data clean rooms, or federated access architectures as alternatives to full data replication
Experience supporting audit preparation, SOP development, or workflow/process redesign
Experience in China-facing R&D operations or global roles that
required
routine coordination on inbound data access for China-based teams
Call to Action: If you are motivated to convert complexity into clarity and create the compliant data pathways that fuel next-generation discovery, we would love to hear how you will make this impact with us! Date Posted 09-set.-2026 Closing Date 22-set.-2026 Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.
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