- 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.