Data Scientist, Computational ADME

AstraZeneca is looking to recruit a Data Scientist, Computational ADME to join the Machine Learning & AI team within AstraZeneca's Drug Safety and Metabolism (DSM) function. The position can be placed either in Gothenburg (Sweden), Cambridge (UK) or Waltham/Boston (US) and will be at a Senior Scientist or Associate Principal Scientist level depending on the candidates experience.

Drug Safety and Metabolism (DSM) is a function within Innovative Medicines and Early Development (IMED), AstraZeneca's small molecule biotech unit which deliver candidate drugs into late-stage clinical development. The role of DSM 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. DSM is committed to be an industry leader in supporting the discovery and non-clinical development of safe medicines that improve patients' lives. Within DSM, the Machine Learning & AI group is dedicated towards developing computational models to aid projects to design and select molecules with the right safety and ADME profiles.

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

Main Duties and Responsibilities

As a Data Scientist in Computational ADME, your main responsibilities will involve:

* Applying Machine Learning / Artificial Intelligence and mechanistic modeling approaches to ADME 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.
* 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

Requirements

* PhD degree (or equivalent) in life science, statistics or computer science, preferably with industry experience
* Demonstrated knowledge in one or more of the following areas: (i) Expertise in a variety of machine learning methods (e.g. Deep Learning, SVM, Random Forrest), (ii) Biological understanding of drug metabolism and pharmacokinetics (DMPK) and modeling of ADME endpoints, (iii) Systems biology / bioinformatics / cheminformatics, (iv) Expertise in high-performance computing and programming (e.g. Python, C++, Java). Experience using cloud platforms are 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.

If you are interested, apply now!

For more information please contact recruiting manager Ernst Ahlberg Helgee,

Welcome with your application no later than September 30th, 2018.

AstraZeneca is a global, science-driven biopharmaceutical company that turn ideas into life changing medicines. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. AstraZeneca is proud to provide a unique workplace culture that inspires innovation and cross-boundary collaboration. Within this environment employees are empowered to express diverse perspectives, and are made to feel valued, energized and rewarded for their ideas and creativity.

AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law.