Data Scientist - Clinical Trial Analytics

Description :

Role Overview :

We are seeking a Data Scientist with strong expertise in clinical trial analytics to support feasibility analysis and site selection initiatives. The ideal candidate will combine data science, AI/ML, and agent-based automation capabilities to develop predictive models and intelligent workflows that enhance clinical trial planning and operational decision-making.

Key Responsibilities :

- Develop and implement analytics models and AI-driven solutions to support clinical trial

feasibility and site selection.

- Build predictive models and decision-support tools leveraging clinical trial operational data.

- Design and implement AI agent-based workflows and automation frameworks to improve data analysis and operational insights.

- Work closely with clinical operations, data management, and trial teams to translate analytical insights into actionable strategies.

- Develop and optimize data pipelines and analytical models using Python and SQL.

- Deploy scalable analytics solutions using modern AI/ML frameworks and cloud-based platforms.

Required Skills & Experience :

- 5+ years of experience in data science or analytics within clinical trial operations.

- Strong experience with clinical trial feasibility analytics and site selection methodologies.

- Proficiency in Python and SQL for data processing, modeling, and analytics.

- Hands-on experience with AI/ML frameworks and predictive modeling techniques.

- Experience developing AI agents, workflow automation solutions, or intelligent analytics systems.

- Prior experience working in a CRO, pharmaceutical, or clinical research environment.

- Strong communication and stakeholder collaboration skills with clinical and cross-functional teams.

Preferred / Nice-to-Have Skills :

- Experience with LangChain, LlamaIndex, or Retrieval-Augmented Generation (RAG) pipelines.

- Exposure to multi-agent systems and AI orchestration frameworks.

- Familiarity with cloud platforms (AWS, Azure, or GCP).

- Experience with containerized deployment (Docker, Kubernetes).