Data Scientist

Job Title:

Location:

Experience:

Compensation:

Joining:

About the Role:

We are looking for a highly skilled and motivated Machine Learning Specialist / Data Scientist with a strong foundation in data science and a deep understanding of clinical supply chain or supply chain operations. This individual will play a critical role in developing predictive models, optimizing logistics, and enabling data-driven decision-making within our clinical trial supply chain ecosystem.

Key Responsibilities:

• Design, develop, and deploy machine learning models for demand forecasting, inventory optimization, and supply chain efficiency
• Analyze clinical trial and logistics data to uncover insights and enable proactive planning
• Collaborate with cross-functional teams including clinical operations, IT, and supply chain to integrate ML solutions into workflows
• Build interactive dashboards and tools for real-time analytics and scenario modeling
• Ensure models are scalable, maintainable, and compliant with regulatory frameworks (e.g., GxP, 21 CFR Part 11)
• Stay up to date with the latest advancements in ML/AI and bring innovative solutions to complex clinical supply challenges

Required Qualifications:

• Master’s or Ph.D. in Computer Science, Data Science, Engineering, or a related field
• 3–5 years of hands-on experience in machine learning, data science, or AI (preferably in healthcare or life sciences)
• Proven experience with clinical or supply chain operations such as demand forecasting, IRT systems, and logistics planning
• Proficiency in Python, R, SQL, and ML frameworks like scikit-learn, TensorFlow, or PyTorch
• Solid knowledge of statistical modeling, time series forecasting, and optimization techniques
• Strong analytical mindset and excellent communication skills
• Ability to thrive in a fast-paced, cross-functional environment

Preferred Qualifications:

• Experience working with clinical trial systems and data (e.g., EDC, CTMS, IRT)
• Understanding of regulatory requirements in clinical research
• Familiarity with cloud platforms such as AWS, Azure, or GCP
• Exposure to MLOps practices for model deployment and monitoring