Group Lead, Computational DMPK & Safety

Cambridge UK, Barcelona ESP, Gothenburg SWE

Competitive Salary + Benefits

Make a more meaningful impact to patients’ lives around the globe.

Here you’ll have the opportunity to make a meaningful difference to patients’ lives.

With science at its heart, this is the place where breakthroughs born in the lab become transformative medicines – for the world’s most complex diseases. Answer unmet medical needs by pioneering the next wave of science, focusing on outcomes and shaping the patient ecosystem.

With our ground-breaking pipeline, the outlook is bright. Be proud to be part of a place that has achieved so much, yet is still moving forward. There’s no better time to join our global, growing enterprise as we lead the way for healthcare and society.

AstraZeneca is looking to recruit a Group Lead in Computational Toxicology & Data Science to join the Imaging & Data Analytics team within the Clinical Pharmacology & Safety Sciences (CPSS) function at AstraZeneca’s vibrant R&D site in Gothenburg, Sweden or Cambridge, UK.

Clinical Pharmacology & Safety Sciences (CPSS) is a function within Biopharmaceutical R&D Unit, AstraZeneca’s biotech unit which delivers candidate drugs into late-stage clinical development. The role of CPSS is to characterize absorption, disposition, metabolism, and elimination (ADME) properties of compounds, non-clinical toxicology, and off-target pharmacology. Working in disease-focused project teams we help to discover efficacious molecules with good safety profiles. CPSS is committed to be an industry leader in supporting the discovery and non-clinical development of safe medicines that improve patients’ lives. Within CPSS, the Computational Toxicology & Data Science team is dedicated towards developing computational models to aid projects to design and select molecules with the right safety and ADME profiles.

As a Group Lead in Computational Toxicology & Data Science, you will work with cutting edge technology in an open and collaborative environment nursing novel ideas. The group has a strong focus on method development within the computational area, acknowledging the need to have an in-depth understanding of the safety or ADME properties of interest. Collaboration within and outside CPSS is essential.

Main Duties and Responsibilities

As a Group Lead in Computational Toxicology & Data Science, your main responsibilities will involve:

• Application of Machine Learning / Artificial Intelligence and mechanistic modeling approaches to ADME and safety endpoints assuring delivery of appropriate tools and in silico models to projects for virtual screening, compound selection and experimental prioritization.

• Analyzing data and model quality ensuring high performance of models and tools

• Providing specialist support for development, interpretation, and application of machine learning models along with knowledge of common database query languages e.g. SQL

• Supporting analyses to provide mechanistic and translational insight into compound ADME and Safety profiles

• Method development to improve models and enhance model value and generated knowledge

• Work directly with the portfolio project teams to evaluate and incorporate model predictions in decisions and publish novel research in high-quality, peer-review journals and scientific conferences as appropriate

Requirements

• PhD degree (or equivalent) in life science, statistics or computer science, preferably with 3+ years of pharmaceutical industry experience

• Demonstrated knowledge in one or more of the following areas

  • Expertise in a variety of machine learning methods (e.g. Deep Learning, SVM, Random Forest)

  • Computational chemistry, Computational toxicology or Cheminformatics

  • Expertise in high-performance computing and programming (e.g. Python, Julia, R, C++, Java). Experience using cloud platforms is beneficial.

• Excellent communication skills and ability to work in a multidisciplinary research environment

• Innovative thinking, with enthusiasm, energy and drive.

• Open-minded, and ready to embrace new ideas and different perspectives.

• Strong critical thinking, planning, organizational and time management skills.

Desirable

• Biological understanding of toxicology and/or drug metabolism and pharmacokinetics (DMPK) and modeling of ADME or safety endpoints

Why AstraZeneca?
At AstraZeneca when we see an opportunity for change, we seize it and make it happen, because any opportunity no matter how small, can be the start of something big. Delivering life-changing medicines is about being entrepreneurial - finding those moments and recognizing their potential.

Join us on our journey of building a new kind of organization to reset expectations of what a bio-pharmaceutical company can be. This means we’re opening new ways to work, pioneering cutting edge methods and bringing unexpected teams together. Interested? Come and join our journey.

So, what’s next!

We look forward to finding out more about you. Send in your application as soon as possible, but no later than the of 30/09/2022. We will review applications continuously.

For more information about the role, the team or working for AstraZeneca, please contact the hiring manager Nigel Greene (https://www.linkedin.com/in/nigel-greene-a618b7/)

Where can I find out more?

Follow AstraZeneca on LinkedIn https://www.linkedin.com/company/1603/

Follow AstraZeneca on Facebook https://www.facebook.com/astrazenecacareers/

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.