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- Aufgaben: Support non-clinical PK and PKPD studies with data analysis and IT infrastructure.
- Arbeitgeber: Join a leading pharmaceutical company in Basel, shaping the future of drug discovery.
- Mitarbeitervorteile: Work in a dynamic environment with opportunities for growth and collaboration.
- Warum dieser Job: Make an impact in healthcare while working with cutting-edge technology and methodologies.
- Gewünschte Qualifikationen: MSc or PhD in life science, computer science, or related fields; programming experience required.
- Andere Informationen: Position starts on 01.01.2024, with a duration of 12 months.
Das voraussichtliche Gehalt liegt zwischen 60000 - 84000 € pro Jahr.
Project:
For our customer, a big pharmaceutical company in Basel, we are looking for a highly qualified DMPK/PD Data Scientist.
Background:
The department 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, preferably 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
- Toxicology
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.
Must Haves:
- 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.
- Experience in pharmaceutical industry, pharmacokinetic evaluation or drug discovery/development.
- Experience in a GMP regulated environment.
Reference Nr.:
923024SGR
Role:
DMPK/PD Data Scientist
Industry:
Pharma
Workplace:
Basel
Pensum:
100%
Start:
01.01.2024
Duration:
12++
Deadline:
22.11.2023
DMPK/PD Data Scientist Arbeitgeber: Itcag
Kontaktperson:
Itcag HR Team
StudySmarter Bewerbungstipps 🤫
So bekommst du den Job: DMPK/PD Data Scientist
✨Tip Number 1
Make sure to highlight your programming skills, especially in Python and R, during any networking opportunities. Engage with professionals in the pharmaceutical industry on platforms like LinkedIn to showcase your expertise and interest in DMPK/PD studies.
✨Tip Number 2
Familiarize yourself with the latest trends in pharmacokinetics and pharmacodynamics. Being able to discuss recent advancements or case studies in these areas can set you apart during interviews and demonstrate your passion for the field.
✨Tip Number 3
Consider reaching out to current or former employees of the company in Basel. They can provide valuable insights into the company culture and expectations, which can help you tailor your approach when applying.
✨Tip Number 4
Prepare to discuss your experience with GMP regulations and how it relates to your previous work. This is crucial for the role, and demonstrating your understanding of compliance in a pharmaceutical setting will strengthen your candidacy.
Diese Fähigkeiten machen dich zur top Bewerber*in für die Stelle: DMPK/PD Data Scientist
Tipps für deine Bewerbung 🫡
Highlight Relevant Experience: Make sure to emphasize your academic background and any industry experience related to pharmacokinetics, programming, or data analysis. Mention specific projects or roles where you utilized Python, R, or SQL.
Tailor Your CV: Customize your CV to reflect the skills and qualifications mentioned in the job description. Focus on your experience with machine learning, chemoinformatics, and any relevant software tools like D360 or Spotfire.
Craft a Strong Cover Letter: Write a compelling cover letter that explains why you are a perfect fit for the DMPK/PD Data Scientist role. Discuss your passion for drug discovery and how your skills align with the responsibilities outlined in the job description.
Proofread Your Application: Before submitting, carefully proofread your application materials for any grammatical errors or typos. A polished application reflects attention to detail, which is crucial in the pharmaceutical industry.
Wie du dich auf ein Vorstellungsgespräch bei Itcag vorbereitest
✨Showcase Your Technical Skills
Make sure to highlight your programming experience, especially in Python and R, as well as any knowledge of SQL. Be prepared to discuss specific projects where you've applied these skills, particularly in the context of pharmacokinetics or pharmacodynamics.
✨Understand the Industry Context
Familiarize yourself with the pharmaceutical industry, especially regarding drug discovery and development processes. Being able to discuss current trends and challenges in pharmacokinetics will demonstrate your genuine interest and understanding of the field.
✨Prepare for Technical Questions
Expect technical questions related to PKPD studies and data analysis workflows. Brush up on your knowledge of PBPK modeling approaches and machine learning applications in pharmacokinetics to confidently answer these questions.
✨Emphasize Collaboration Experience
Since the role involves liaising with external collaborators and internal stakeholders, be ready to share examples of how you've successfully worked in teams. Highlight any experience you have in developing standardized protocols or validating reports collaboratively.