Description

Build and maintain scalable data engineering and machine learning pipelines on the Databricks Lakehouse platform for pharmaceutical and life sciences use cases.

This role is on-site.

Responsibilities

• Design and optimize Spark jobs and Delta Live Tables (DLT) pipelines for performance and cost efficiency across large-scale datasets.
• Implement end-to-end ML workflows using MLflow, Spark ML, and Python, covering data ingestion, feature engineering, training, and deployment.
• Apply data governance and security standards using Unity Catalog, ensuring compliance with HIPAA, GxP, and 21 CFR Part 11 regulations.
• Develop predictive models for patient identification, market access analytics, and pharmacovigilance, incorporating NLP and LLM techniques.
• Collaborate with data engineers and business stakeholders to translate complex requirements into production-ready analytics and Genie-enabled insights.

Required Skills

• 5+ years of professional experience in data science or advanced analytics.
• Strong proficiency in Python, including PySpark, Pandas, NumPy, and Scikit-learn.
• Hands-on experience with Databricks, Apache Spark, and Delta Lake for large-scale data processing.
• Solid understanding of SQL and data modeling for analytical and reporting workloads.
• Experience with Azure or AWS cloud platforms and data engineering concepts.
• Ability to work effectively with Data Engineers to ensure high-quality, production-ready datasets.
• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Engineering, or a related field.

Preferred Skills

• Experience in the pharma, biotech, or life sciences industry.
• Knowledge of NLP, generative AI, and RAG pipelines.
• Familiarity with real-world data (RWD) and clinical trial datasets.

Referral Bonus: ₹50,000

Key Skills

Education

Bachelor's or Master's degrees