Senior Manager, Clinical Data Science

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Manager, Clinical Data Science based in the United States. This is a senior data science role focused on advancing clinical drug development through predictive modeling and sophisticated analysis of complex, multimodal datasets. You’ll work with clinical, imaging, genomic, and other high-dimensional data to generate insights into disease characteristics and treatment response. The position combines hands-on technical expertise with cross-functional collaboration across data science, translational sciences, IT, and software engineering teams. You’ll design and deploy scalable data processing and analytical pipelines across cloud and high-performance computing environments. You’ll also help shape modern visualization and prediction tools that enable project teams to work more effectively with complex data. This is an opportunity to introduce innovative data science capabilities in a research-driven pharmaceutical environment. The role is well suited to an experienced technical leader who is motivated by applying advanced analytics and emerging AI approaches to meaningful healthcare challenges. Lead data science initiatives that support clinical drug development, with a focus on predictive modeling of disease characteristics and drug response. Process, integrate, and analyze high-dimensional multimodal datasets, including clinical, imaging, genomic, text, and other real-world data sources. Develop sophisticated predictive models using machine learning, deep learning, natural language processing, and related advanced analytical methods. Design, build, deploy, and support scalable data engineering and data science platforms for research and clinical development projects. Develop analysis pipelines for complex datasets such as genomics and imaging using cloud infrastructure and high-performance computing environments. Build and deploy interactive web-based interfaces for statistical models, visual analytics, and predictive applications using tools such as Streamlit, R-Shiny, or comparable technologies. Work closely with data scientists, translational sciences teams, IT, and software engineering partners to deliver secure, scalable, and effective solutions. Translate complex analytical approaches and findings into practical solutions that support project teams and broader drug development objectives. Identify opportunities to improve data processing, analytical methodologies, visualization, and deployment capabilities across the organization. Proactively propose and implement initiatives that increase efficiency, strengthen internal data science capabilities, and enable more effective project delivery. Contribute to the evaluation and adoption of emerging technologies, including state-of-the-art agentic AI approaches where relevant. Coordinate with IT and software engineering teams, where needed, to maintain secure, compliant, reliable analytical tools and applications. Communicate technical strategies, analytical findings, and project outcomes clearly to both technical and non-technical stakeholders. Requirements Minimum of 6 years of relevant professional experience in data science, data engineering, computational science, or a closely related discipline. Master’s degree required; Ph.D. preferred, particularly in Engineering, Computer Science, or a related technical field. Strong programming experience with at least two languages or technologies such as R, Python, and Shell scripting. Proven experience developing, deploying, and supporting data engineering and data science platforms. Hands-on experience working with unstructured and high-dimensional data, including text, imaging, omics, or comparable real-world datasets. Strong knowledge of machine learning, deep learning, NLP, predictive modeling, or other advanced data science methodologies. Demonstrated experience creating data analysis pipelin