Postdoctoral Fellow – Oncology – Machine Learning

Job Title: Postdoctoral Fellow – Oncology – Machine Learning
Location: Mississauga, ON
Term: 1 year contract term, with the strong possibility of extension for a 2nd year

At AstraZeneca, we pride ourselves on crafting a collaborative culture that champions knowledge-sharing, ambitious thinking and innovation – ultimately providing employees with the opportunity to work across teams, functions and even the globe. 

Recognizing the importance of individualized flexibility, our ways of working allow employees to balance personal and work commitments while ensuring we continue to create a strong culture of collaboration and teamwork by engaging face-to-face in our offices 3 days a week. Our head office and BlueSky Hub in downtown Toronto are purposely designed with collaboration in mind, providing space where teams can come together to strategize, brainstorm and connect on key projects. 

Our dedication to sustainability is also central to our culture and part of what makes AstraZeneca a great place to work. We know the health of people, the planet and our business are interconnected which is why we’re taking ambitious action to tackle some of the biggest challenges of our time, from climate change to access to healthcare and disease prevention. 

Introduction to Role:

Are you passionate about the potential of data and Artificial Intelligence (AI) as a transformative force in the medical field? We are excited to offer an opportunity for a recent PhD graduate in computer science or data science to join our Oncology R&D Team. This role involves a joint project with Rigshospitalet, Copenhagen University Hospital, Denmark, specializing in Machine Learning for Predicting Adverse Events in Blood Cancer Treatments.

We seek a talented Postdoctoral Data Scientist to develop a methodology for risk assessment aimed at guiding personalized treatment decision-making for patients with blood cancer. The successful candidate will collaborate with Professor Niemann's laboratory at Copenhagen University Hospital, leveraging advanced analytical techniques, including machine learning, deep learning, and graph theory. This will involve utilizing unique Danish Health Registry data, Electronic Health Record (EHR) data, and internal AstraZeneca data.

Moderate to severe adverse events while treating blood cancers have the potential to necessitate treatment changes, which in turn could reduce efficacy, impact quality of life, and increase the economic burden associated with their management. Developing precise, personalized predictive algorithms capable of identifying patients who are likely to experience serious adverse events with one treatment as opposed to another can significantly influence patient management strategies, ultimately leading to improved patient outcomes.

This position will require at least 1 week of travel to Denmark for training purposes.

Accountabilities:

As part of this role, you will work alongside technical and domain experts, contribute to scientific strategies, develop algorithms for individualized decision support, and employ a range of machine learning techniques to reveal unique patterns and actionable insight impacting research and clinical practices. You will also have the opportunity to publish cutting edge research, code and scientific discoveries in high-impact journals, meetings and repositories.

• Work alongside technical and domain experts, including drug project teams and the burgeoning Data Science and AI community across AZ and Rigshospitalet.
• Contribute to scientific strategies & provide a rationale for targeting specific patient cohorts in studies
• Develop algorithms that can be implemented to improve benefit-risk for treatment based on individualized decision support
• Employ a range of machine learning techniques to reveal unique patterns and actionable insight impacting research and clinical practices.
• Publish cutting edge research, code and scientific discoveries in high-impact journals, meetings and repositories.

Essential Skills/Experience:

• A PhD in a quantitative discipline (Computer Science, Data Sciences, Mathematics, Physics, Computational Statistics or similar)
• Excellent communication and presentation skills
• Expertise solving big/complex data problems with state-of-the-art analytical techniques, particularly in machine/deep learning (preferably for biological/health data e.g., genomics)
• High programming proficiency (Python and/or R)

Desirable Skills/Experience:

• Experience working with health data or biological data
• Experience contributing to the research community through re-useable code and publication

At AstraZeneca, we are united by a common purpose: to push the boundaries of science to deliver life-changing medicines. Our Oncology R&D team is dedicated to eliminating cancer as a cause of death. We are pioneers of collaborative research, fusing academia and industry, and have built an unrivalled scientific community both internally and externally. We make bold decisions driven by patient outcomes and are backed by significant investment. Join us and be part of the team committed to improving the lives of millions with cancer.

Are you ready to follow the science and pioneer new frontiers? Apply today and join our team!

Great People want to Work with us! Find out why: 

• GTAA Top Employer Award for 10 years 

• Top 100 Employers Award 

• Canada’s Most Admired Corporate Culture 

• Learn more about working with us in Canada 

• View our YouTube channel 

Are you interested in working at AZ, apply today! 

AstraZeneca is an equal opportunity employer that is committed to diversity and inclusion and providing a workplace that is free from discrimination. AstraZeneca is committed to accommodating persons with disabilities. Such accommodation is available on request in respect of all aspects of the recruitment, assessment and selection process and may be requested by  emailing AZCHumanResources@astrazeneca.com. 

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.