Director of Decision Science - Statistical Innovation
AstraZeneca

Job Info

---

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.

We are recruiting a Decision Science Director to join our growing Statistical Innovation group. Our main focus is providing statistical methodology support for all phases of clinical development for AZ's Cardiovascular, Renal & Metabolism and Respiratory & Immunology divisions together with specific targeted support for the Oncology division and the newly acquired Alexion division of AZ which specializes in medicines to treat rare diseases. You will complement the skills in the group by providing decision science solutions to problems faced in clinical development.

In this role, you will belong to the Early Biometrics & Statistical Innovation (EB&SI) department, where our data-centric focus helps us work efficiently and creatively to bring the right medicines to the right patients. Our teams use their expertise in statistics and programming to address drug development objectives and reduce uncertainty in product development, driving better business decisions with quantitative reasoning. As part of the Data Science & AI team, you'll use technology at the forefront of science in a creative environment, with the scope to develop new statistical ideas and apply them in your work.

Main duties and responsibilities

You will provide key input as you use decision science to find solutions to problems at critical stages in the drug development cycle. Your key focus will be on producing pragmatic solutions, often within a tight time scale where the emphasis will be to deliver first, then refine and develop your solutions thereafter.

You will lead capability build in more than one of the following areas of decision science:

• Create structure and insights to decision problems using decision science techniques
• Design of early and late phase clinical studies using both frequentist and Bayesian approaches
• Determine Probability of Technical Success using elicitation and assurance
  

You will also provide expert consultancy on key issues for medical scientists working on clinical development plans and other key roles across the business where decision science can add value. This will involve leading and participating in strategic activities such as capability build projects, which can directly impact improvements to the way clinical trials are crafted and analysed in AstraZeneca. You will also be expected to interact with external scientists and regulators, through publications, presentations and cross-industry collaborations. There will also be a focus to proactively identify new areas where Decision science can contribute, developing collaborative relationships with the external scientific/academic community.

Requirements

Essential

• Post-graduate degree in Statistics or a similar area with 5+ years' experience delivering innovative solutions to sophisticated decision making problems in an applied environment.
• Knowledge of decision making tools, elicitation and methods to provide structure to complex problems.
• Knowledge of elicitation, scenario planning, objective networks, strategy tables, multi criteria decision analysis, decision quality, objective networks, framing sessions, decision trees, design thinking and mind mapping
• Solid understanding of programming in Excel and R and/or Python
• High level of competence in global and cross-skilled collaborative working
• Track record of research and methodological development in Decision Science, supported by scientific publications in first class journals
• Desire to apply your scientific competence on practical problems, for the benefit of patients
  

Desirable

• Broad awareness of decision sciences issues against the evolving scientific and regulatory landscape
  

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

• GTAA Top Employer Award for 9 years
• Learn about our 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.

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.

This job has expired.