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Data Scientist - Clinical Trial Analytics

vueverse.
Hyderabad, Telangana, INPosted March 16, 2026

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Job 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 capabilitiesto 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).

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