Job Title: Data Analyst - Pharmaceuticals (B.Pharm Expert)
Location: Agra
Experience Level: 5+ years
About the Role
We are seeking a B.Pharm-qualified Data Analyst to leverage data analytics for pharma insights, including sales forecasting, clinical trial analysis, and supply chain optimization. You will combine domain expertise with technical skills to drive data-driven decisions in drug development, market strategy, and regulatory compliance.
Key Responsibilities
- Extract, clean, and analyze large datasets from sales, clinical trials, manufacturing, and pharmacovigilance using SQL and Python.
- Develop dashboards and reports on key metrics (e.g., drug efficacy, market share, adverse events) using Tableau/Power BI.
- Perform statistical analysis, predictive modeling, and trend forecasting for product lifecycle management.
- Support regulatory submissions with data validation per ICH guidelines and 21 CFR Part 11.
- Collaborate with R&D, sales, and quality teams to identify insights from pharma-specific data (e.g., IQVIA, IMS).
- Automate reporting pipelines and ensure data integrity/security in compliance with pharma standards.
- Conduct ad-hoc analyses on drug pricing, inventory, or patient outcomes.
Required Skills & Qualifications
- B.Pharm (mandatory) with 5+ years of data analytics experience, preferably in pharmaceuticals, biotech, or healthcare.
- Proficiency in Python (Pandas, NumPy, SciPy, Matplotlib/Seaborn) for data manipulation and modeling.
- Advanced SQL skills for querying databases (e.g., PostgreSQL, MySQL, Oracle).
- Expertise in visualization tools: Tableau, Power BI, or Google Data Studio.
- Strong knowledge of pharma domain: clinical data, PK/PD studies, regulatory reporting, and stats (hypothesis testing, regression).
- Experience with big data tools (e.g., Spark, Hadoop) or cloud platforms (AWS/GCP) preferred.
- M.Sc. Statistics/M.Pharm (Analytics) or relevant certifications (e.g., Google Data Analytics) as a plus.
Preferred Skills
- Machine learning basics (Scikit-learn) for pharma applications like patient segmentation.
- Familiarity with EDC systems (e.g., Medidata Rave), SAS, or R programming.
- Publications or projects in pharma data analysis.
