Associate/Senior Data Scientist in Machine Learning for Merck in Merck EPFL Lausanne

veröffentlicht
Kontakt
Monika Kowalska, Randstad Merck Aubonne
Jobtyp
Temporär
jetzt bewerben

job details

veröffentlicht
Ort
Merck EPFL Lausanne, Waadt
Sektor
Administration
Jobtyp
Temporär
Referenznummer
129085-39178
Kontakt
Monika Kowalska, Randstad Merck Aubonne
Telefon
+41 21 900 35 28

Stellenbeschreibung

We're currently looking for talented scientists with sound experience in Machine Learning (ML) techniques to join our Client, Merck Innovation Digital Lab based at the EPFL campus, Lausanne, Switzerland.

There is an opening for full-time, 1-year (with optional 2nd year) Data Scientist (from Associate to Senior depending on candidate level) in the application of Artificial Intelligence (AI) in Quantitative Pharmacology.

Your Role:

In this role, you will apply your practical experience in mining and analyzing various kind of data, your deep understanding of ML algorithms, and programming skills to solve challenging real-world problems at a large scale. You will apply AI/ML/deep learning (DL) to enhance model-informed drug discovery and development. Projects may include, but are not limited to, the analysis of translational, biomarker and clinical data, use of ML to improve patient selection, exposure predictions, and trial designs.

As highly motivated and intellectually curious scientist, you will work in a stimulating and engaging environment and have close interactions with cross-functional team experts in drug development.

Qualifikation

Who You are:

  • PhD in computer science, mathematics, engineering, or similar, with demonstrated experience and sharp skills in ML/DL domains.
  • Proficiency in programming languages supporting ML, like Python, R and C/C++.
  • Industry experience would be an asset, particularly in Life Sciences or Biopharma R&D.
  • Experience in PKPD mathematical modeling, particularly in Pharmacometrics, would be an asset.
  • Previous experience in scientific project management is a plus.
  • Fluent in English with good communication skills.
  • Demonstrated ability to work independently, be proactive and take ownership.
  • Strong problem-solving skills and ability to analyze complex problems.