Data Scientist - Clinical Trial Analytics

As a Data Scientist at our company, you will be responsible for leveraging your expertise in clinical trial analytics to support feasibility analysis and site selection initiatives. You 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.

Qualifications Required: - 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.

In addition, the company prefers candidates with the following 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). As a Data Scientist at our company, you will be responsible for leveraging your expertise in clinical trial analytics to support feasibility analysis and site selection initiatives. You 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.

Qualifications Required: - 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.

In addition, the company prefers candidates with the following 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).