Introduction to role

In 2025 within just the United States, over 2 million people are projected to be diagnosed with cancer. Along with heart disease, cancer is the leading cause of death across all ages. Cancer patients’ remaining lives are often measured in months, not years. How can we extend the recent advances in ML and AI to tackle cancer? How can we determine which patients should receive which drugs, especially if the drugs are still being developed? Or whether the drugs will be safe? Or how much or how often a drug should be taken? Or what combination of drugs?

Accountabilities

The AI Research team in Oncology reports directly to the Chief Data Scientist, with a broad remit to develop and deliver new algorithms, models, and research that can propel the next generation of Oncology drug discovery and development. Join us to build the future of AI in biology. Join us to solve cancer.

You will help build next-generation AI models to power AstraZeneca’s bold ambition to launch 20 new medicines by 2030. You will bring your deep knowledge of probabilistic modeling as applied to a diversity of biological sequence modalities to accelerate delivery of next-generation foundation models. You will leverage data from real patients tested with real drugs to model and understand treatment outcomes. You will devise creative solutions to scale up and model multimodal datasets (e.g., DNA, RNA, protein, tumor imaging, tissue imaging, doctor’s notes). You will work collaboratively in a close-knit team, bringing your unique skillset, and with a sense of urgency to do what it takes for the team to win.

Essential Skills/Experience

• PhD in a computational discipline (e.g., bioinformatics, computer science, computational biology, computational neuroscience, physics, mathematics) and at least 0-2 years’ additional experience in a machine learning & AI research & development setting
• Exceptional software engineering skills; knowledge of computing hardware a plus
• Deep understanding of machine learning and bioinformatics, plus technical domain expertise in one or more of the following areas:​

  o Computational molecular and structural biology

  o Probabilistic machine learning for biological sequences (e.g., Bayesian statistics, variational inference, diffeomorphic transformations)

  o Language models as applied to biology, in particular genomic and protein sequences (e.g., protein language models, embeddings)

  o Generative modeling

  o Uncertainty quantification and interpretability

  o LLMs and transformer models (algorithms, training, fine-tuning)

  o Multimodal modeling (e.g., vision-language models, multi-task models)

  o Datasets and benchmarks

Soft skills:
  o Excellent written and verbal communication skills
  o Deep, up-to-date knowledge of ML literature and connections with the ML community (e.g., conference publications, presentations, or collaborations)
  o A problem-solving mindset
  o Integrity, responsibility, humility, and open-mindedness
  o Initiative, proactivity, practicality, independence, and ownership
  o Team-oriented mindset
  o Ability to execute and iterate at pace

Desirable Skills/Experience

Feel the full weight of AstraZeneca's support behind what we're doing. Our work enables so many other people to scale and run efficiently with big data in software systems and processes. It means we speed up processes and quality decision-making. Here, the impact of our changes is recognized from the top.

Ready to make a difference? Apply now!

Date Posted

Closing Date

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.