Senior Manager, Clinical Data Science

Committments

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.

Cook up with IT and software engineering teams, where needed, to maintain secure, compliant, reliable analytical tools and applications.

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 pipelines using cloud servers and high-performance computing clusters.

High proficiency with Linux and Windows operating systems and experience working within AWS environments.

Experience developing front-end or web-based interfaces for statistical and predictive models.

Strong ability to create visual analytics and deploy interactive prediction applications using Streamlit, R-Shiny, or similar technologies.

Experience developing and deploying cloud-based tools or solutions within distributed computing environments.

Strong ability to collaborate with IT, software engineering, data science, and scientific teams to deliver robust technical solutions.

Excellent written, verbal, communication, and presentation skills, with the ability to explain sophisticated technical concepts clearly.

Proactive, creative, and solutions-oriented approach to identifying improvements and introducing new analytical capabilities.

Preferred: Experience with state-of-the-art agentic AI technologies.

Preferred: Experience managing or coordinating with IT and software engineering teams to maintain secure and compliant tools and applications.