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.