Randstad Switzerland

DMPK/PD Data Scientist

Job description

Background

The Department of Translational PKPD & Clinical Pharmacology (PNK) is looking for a candidate to support non-clinical PK and PKPD studies. The person will support front- and back-end IT infrastructure related to planning, execution, analysis, and database curation of non-clinical pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PKPD) studies.

The Perfect Candidate

Has an academic Degree, preferable MSc. or PhD degree in life science, computer science, mathematics, or statistics, preferably with industry experience and knowledge in one or more of the following areas: programming (e.g. Python, R, C++, Java, SQL), machine learning, chemoinformatics, computational chemistry, biology, or toxicology.

General Information

  • No sponsorship for non-EU citizen
  • Start date: asap
  • Latest Start Date: 01.02.2024
  • Planned duration: 1 year
  • Extension: very likely
  • Workplace: Basel
  • Workload: 80-100%
  • Home Office: up to 2 days per week
  • Team: 75 team members
  • Working hours: Standard

Tasks & Responsibilities

  • Support of non-clinical PK and PKPD studies.
  • Support front- and back-end IT infrastructure related to planning, execution, analysis, and database curation of non-clinical pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PKPD) studies.
  • Development of standardized PK protocols with external collaborators (CROs) to reduce turnaround time.
  • Validation of external PK reports in collaboration with internal stakeholders and study monitors.
  • Support tools and work as PK customer representative for automated data analysis and prediction tools such as integrated high throughput PBPK models.
  • Integration of available in vitro and in vivo non-clinical data. Integration and visualization of available data within databases and maintenance of software used by drug discovery teams (e.g. D360, Spotfire).
  • Liaise data analysis workflows with other functions such as PBPK modeling approaches., machine learning (ML) models and PKPD models (built in R, Berkeley-Madonna, Phoenix WinNonlin, Monolix, Morphit).
  • Automation of TK analysis and reporting

Needed Expertise

  • Academic degree, pref. MSc or PhD degree in life science, computer science, mathematics, or statistics
  • Min. 1-2 years experience in programming (e.g. Python, R, C++, Java, SQL)
  • Ability to perform PBPK analysis
  • Fluent in English
  • Experience in machine learning, chemoinformatics, computational chemistry, biology, or toxicology is a plus
  • Experience in the pharmaceutical industry, pharmacokinetic evaluation, or drug discovery/development is a plus
  • Experience in a GMP-regulated environment is a plus

We are looking forward to receiving your application!

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