Senior Data Scientist

Discover International; Paris; Sur place; EUR 50 000 - 80 000; Plein temps

Résumé du poste

A leading company in pharmaceutical analytics is seeking a Data Analyst to manage and analyze extensive datasets related to clinical trials and market data. The role demands collaboration with scientists and executives to align insights with pipeline strategy, alongside developing predictive models. The ideal candidate will have a Master's or PhD in Data Science or a related field, with significant experience in data analytics within pharma. Proficiency in Python, R, and SQL is crucial, complemented by familiarity with cheminformatics tools and strong communication skills to convey technical findings. This position offers a dynamic environment focusing on drug discovery and analytics.

Qualifications

• Master's or PhD in Data Science, Bioinformatics, Computational Biology, or similar.
• 3+ years in pharma/biotech analytics or drug development.
• Technical proficiency in Python, R, SQL, and cheminformatics.

Responsabilités

• Manage and analyze pharmaceutical datasets to identify promising drug candidates.
• Develop machine learning models predicting the success and market potential of assets.
• Work with scientists to align data insights with pipeline strategy.
• Monitor industry trends and suggest partnership opportunities.
• Develop dashboards to present findings clearly.

Connaissances

Python

R

SQL

Machine Learning

Natural Language Processing (NLP)

Data Visualization

Tableau

Power BI

Cheminformatics

Strategic Insight

Communication Skills

Analytical Thinking

Team-oriented

Formation

Master’s / PhD in Data Science

Bioinformatics

Computational Biology

Outils

RDKit

Bioconductor

AWS

Azure

GCP

Description du poste

• Data Analysis : Manage and analyze pharmaceutical datasets (clinical trials, patents, research, market data) to identify promising drug candidates.
• Predictive Modeling : Develop or collaborate with vendors to create machine learning models predicting the success and market potential of assets.
• Collaboration : Work with scientists and executives to align data insights with pipeline strategy.
• Competitive Insights : Monitor industry trends, identify gaps in therapeutic areas, and suggest partnership or acquisition opportunities.
• Data Sourcing & Cleaning : Process and analyze data from various sources (FDA, EMA, PubMed, pharma databases).
• Visualization & Reporting : Develop dashboards and reports to present findings clearly.

Key Skills :

• Technical : Proficient in Python, R, SQL, and machine learning for predictive analytics and natural language processing (NLP).
• Pharma Tools : Familiarity with pharma databases and cheminformatics tools (e.g., RDKit, Bioconductor).
• Data Visualization : Skilled in tools like Tableau, Power BI, Matplotlib, Plotly.
• AI Expertise : Knowledge in AI for drug development is a plus.
• Vendor Management : Ability to oversee and manage vendors and suppliers.
• Strategic Insight : Understanding of data science trends and their application in pharma.

Domain Knowledge :

• Therapeutics : Knowledge of disease biology, drug mechanisms, and pharmacokinetics.
• Regulatory : Familiarity with FDA / EMA approval processes and clinical trials.
• Business Acumen : Understanding of pharma M&A trends and partnerships.

Soft Skills :

• Strong communication skills to translate technical findings to business strategy.
• Analytical thinking and problem-solving in uncertain data scenarios.
• Team-oriented and motivated to contribute in a biotech environment.

Requirements :

• Education : Master’s / PhD in Data Science, Bioinformatics, Computational Biology, or similar.
• Experience : 3+ years in pharma / biotech analytics or drug development.
• Technical Proficiency : Expertise in Python, R, SQL, and cheminformatics.
• Domain Knowledge : Familiarity with clinical trials, regulatory processes, and therapeutic areas.

Preferred Qualifications :

• Experience with pharma datasets (e.g., IQVIA, Clarivate).
• Knowledge of emerging trends like AI-driven drug discovery.
• Familiarity with cloud platforms (AWS, Azure, GCP).
• Ongoing commitment to professional development.