At a Glance
- Tasks: Lead AI research to transform complex data into impactful drug development decisions.
- Company: AstraZeneca, a leader in innovative healthcare solutions.
- Benefits: Hybrid work model, continuous learning, and access to diverse datasets.
- Other info: Collaborative culture with opportunities for mentorship and publication.
- Why this job: Drive breakthrough AI research that directly improves patient outcomes.
- Qualifications: PhD or MSc with relevant experience in machine learning and AI applications.
The predicted salary is between 100000 - 150000 € per year.
This position focuses on guiding AI research that converts complex, multimodal data into decisions to accelerate drug development and improve patient outcomes. It is a fantastic opportunity to thrive in an environment where bold ideas evolve into deployed models and peer-reviewed science, working alongside leading scientists, engineers, and product leaders!
As the Director of EAI, AI Research, the objective is to lead a high-calibre, interdisciplinary team advancing machine learning across multiple therapeutic areas. The role entails setting the research agenda, inventing new methods, and transforming prototypes into robust, governed solutions that support imaging, diagnostics, and clinical validation pipelines. This offers a unique chance to build strategy from the ground up while staying hands-on with cutting-edge methodologies.
Accountabilities
- Research Strategy: Lead the roadmap for priority problem spaces, supporting teams to develop high-value AI capabilities using modern engineering standards.
- Multimodal ML Innovation: Invent and apply methods in deep, representation, reinforcement, and active learning, tailoring metrics to domain-specific challenges.
- Translational Pipeline Impact: Convert scientific advances into production-grade models that improve decision quality and automate key discovery steps.
- ML Ops and Governance: Establish robust practices for model tracking, governance, and lifecycle management, ensuring responsible AI use.
- Cross-Functional Partnership: Advise and co-create with partners across therapeutic areas, translating business needs into effective ML solutions.
- Scientific Contribution: Mentor researchers, publish in top-tier venues, and represent AstraZeneca at leading conferences!
- Stakeholder Alignment: Maintain clear, multidirectional communication across the organisation regarding goals, risks, and results.
Essential Skills and Experience
- Academic Background: PhD in computer science, statistics, applied mathematics, or a related area (or an MSc with 5 years of relevant background).
- Industry Experience: Minimum of 2 years developing machine learning models in an industry setting.
- Machine Perception: Expertise with methods in at least one relevant modality, alongside explainability techniques.
- Domain Application: Experience applying AI to fields such as pathology imaging, radiological analysis, diagnostics, prognostics, or clinical Quality Control.
- ML Ops Knowledge: Practical experience with model tracking, governance, and managing multiple models in diverse production contexts.
- Technical Leadership: Proven ability to lead teams through complex scientific and research efforts.
Desirable Skills and Experience
- Publication Record: A strong research programme demonstrated by prestigious publications (e.g., Nature Machine Intelligence, NeurIPS, ICML) with at least one as lead author.
- Domain Expertise: Deep understanding of the drug development or clinical trial processes.
- Collaborative Delivery: A track record of successfully working with AI engineering teams to deploy complex predictive algorithms.
Working Environment
- Collaborative Culture: Bringing unexpected teams together sparks bold thinking. To facilitate this, we operate on a hybrid model, working an average of three days per week from the office while respecting individual flexibility.
Why AstraZeneca
AstraZeneca pairs cutting-edge AI with profound scientific depth to deliver life-changing insights. The culture emphasises continuous learning, ethical standards, and doing things the right way. Access to rich, diverse datasets provides the foundation to build advanced enterprise analytics.
Equal Opportunity and Accommodations
AstraZeneca is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
Call to Action
If the prospect of leading breakthrough AI research and converting it into tangible patient outcomes excites you, apply today to drive what comes next!
Director, AI Research in Cambridge employer: AstraZeneca
AstraZeneca is an exceptional employer that fosters a collaborative culture where innovative ideas flourish, particularly in the field of AI research aimed at transforming drug development and enhancing patient outcomes. With a strong emphasis on continuous learning and ethical practices, employees benefit from access to diverse datasets and opportunities for professional growth, all while enjoying a flexible hybrid working model that respects individual needs.
StudySmarter Expert Advice🤫
We think this is how you could land Director, AI Research in Cambridge
✨Tip Number 1
Network like a pro! Reach out to people in your field, especially those already at AstraZeneca. A friendly chat can open doors and give you insider info about the role.
✨Tip Number 2
Show off your expertise! Prepare to discuss your past projects and how they relate to the job. Be ready to dive into technical details and demonstrate your problem-solving skills.
✨Tip Number 3
Practice makes perfect! Conduct mock interviews with friends or mentors. This will help you articulate your thoughts clearly and confidently when it’s your turn in the hot seat.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Director, AI Research in Cambridge
Some tips for your application 🫡
Show Off Your Expertise:Make sure to highlight your academic background and industry experience in AI research. We want to see how your skills align with the role, so don’t hold back on showcasing your PhD or relevant MSc, along with any hands-on experience you have in developing machine learning models.
Tailor Your Application:When applying, tailor your application to reflect the specific requirements of the Director, AI Research role. Use keywords from the job description to demonstrate that you understand what we’re looking for and how you can contribute to our mission.
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that are easy to read. Make sure to communicate your ideas effectively, especially when discussing your research strategy and innovative methods.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our culture and values while you’re at it.
How to prepare for a job interview at AstraZeneca
✨Know Your Research Inside Out
Before the interview, dive deep into your past research and projects. Be ready to discuss how your work aligns with the role's focus on AI in drug development. Highlight specific methodologies you've used and their impact on patient outcomes.
✨Showcase Your Leadership Skills
As a Director, you'll need to demonstrate your ability to lead interdisciplinary teams. Prepare examples of how you've successfully guided teams through complex projects, especially in machine learning or AI. Emphasise your experience in mentoring and fostering collaboration.
✨Understand the Business Needs
Familiarise yourself with AstraZeneca's goals and challenges in AI research. Be prepared to discuss how you can translate business needs into effective ML solutions. This shows that you’re not just technically savvy but also understand the bigger picture.
✨Prepare for Technical Questions
Expect to face technical questions about machine learning methods, model governance, and lifecycle management. Brush up on your knowledge of deep learning, reinforcement learning, and explainability techniques. Being able to articulate these concepts clearly will set you apart.
