Job Title:
Data Scientist LeadResponsibilities:
- 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.