Senior Data Scientist

Job Title: Data Scientist Lead

Responsibilities:

• 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.