AstraZeneca
(Senior)Scientist,AIAlliance/Innovation
Neural analysis suggests this role is
optimal for Senior candidates.
“(Senior)Scientist, AI Alliance/Innovation at AstraZeneca. Skills: AI Alliance, Innovation, Machine Learning, Data Science. Identify academic and industry leads. Evaluate data readiness”
What You'll Achieve.
Number of sourced partnerships; Quality of sourced partnerships; Impact of sourced partnerships; Number of executed pilots; Quality of executed pilots; Impact of executed pilots; Time-to-assessment; Clarity of go/no-go decisions; Adoption of successful solutions; Partner satisfaction; Relationship health; External visibility
Industry & Context.
Shanghai-based, Occasional travel
What They're Looking For.
Must Have
Master's degree or equivalent experience, Hands-on ML/deep learning experience, Proficiency in Python and ML frameworks, Collaborative work in fast-paced teams, Cross-geographical stakeholder management, Clear and effective communication skills, Mandarin proficiency
Nice to Have
Experience running collaborative pilots, Familiarity with IP, licensing, agreements, Familiarity with cloud computing, Familiarity with data engineering, Familiarity with interoperability standards, Knows how to set up local agents/models, Publication record in AI/computational biology, Contributions to consortia or standards, Understanding of biologics drug discovery workflows, Knowledge of protein modeling, Knowledge of structure prediction, Knowledge of de novo design, Knowledge of agentic AI approaches
What You'll Do.
Identify academic and industry leads
Evaluate data readiness
Evaluate integration feasibility
Evaluate regulatory posture
Develop recommendations
Develop business cases
Define success metrics
Ensure robust benchmarking
Coordinate data access
Coordinate data curation
Align on interoperable formats
Integrate external models
Integrate external outputs
Drive adoption of pilots
Define scale-up plans
Define change management
Ensure reproducibility
Bridge computational and experimental domains
Partner with wet lab scientists
Guide experimental design
Guide data generation
Align with bioinformatics
Align with protein engineering
Align with structural biology
Align with data engineering
Align with Procurement
Establish joint roadmaps
Establish operating cadences
Establish relationship health
Safeguard AZ interests
Represent AZ in AI consortia
Contribute to publications
Contribute to patent filings
Contribute to strategic initiatives
Amplify outcomes from pilots
Amplify outcomes from partnerships
Develop local support
Navigate region-specific regulatory considerations
Navigate region-specific data considerations
How You'll Work.
Team & Collaboration
Internal wet-lab scientists; Internal AI/data scientists; Bioinformatics; Protein engineers; Structural biologists; Cross-functional teams; Computational domains; Experimental domains; Data engineering; IT; Legal; Procurement
Communication Scope
Clear communication; Effective communication; Bilingual communication
Process & Methodology
Agile delivery
Full Job Description
**About the Role:** We are seeking creative, and collaborative Scientists/Senior Scientists to join the team with a dedicated focus on AI Alliance & Innovation. This role will accelerate AstraZeneca’s biologics discovery and engineering by scouting, evaluating, and integrating external AI capabilities across academia, startups, and technology vendors—while closely partnering with internal wet-lab scientists, AI/data scientists, bioinformatics, protein engineers, and structural biologists across the UK, US, and China. You will translate scientific needs into partnership opportunities, co-design agile pilots to validate cutting-edge methods and drive successful solutions into practice. This is an opportunity to shape the future of biologics through external innovation, ecosystem engagement, and impactful AI-driven science within AstraZeneca’s research network. **Key Responsibilities** * Opportunity sourcing and due diligence: Proactively identify academic and industry partners; lead technical and scientific assessments, evaluate data readiness, integration feasibility, IP posture, and regulatory considerations; develop clear recommendations and business cases. * Pilot co-design and execution: Co-create and run time-bound pilots with partners and internal teams; define hypotheses, success metrics, datasets, guardrails, and delivery plans; ensure robust benchmarking against internal baselines and state-of-the-art. * Data and model integration: Coordinate data access, curation, and governance; align on interoperable formats and pipelines to integrate external models and outputs with AZ systems and workflows. * Translation to impact: Drive the adoption of successful pilots into discovery and engineering workflows; define scale-up plans, change management, training, and monitoring; ensure reproducibility, documentation, and compliance. * Cross-functional collaboration: Bridge computational and experimental domains—partner with wet lab scientists to guide experimental desig
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