Manager, Clinical Data Scientist
- India
Posted 4 days ago
About the role
By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use. I further attest that all information I submit in my employment application is true to the best of my knowledge.
Job Description
Objective / Purpose:
Describe at the highest level the team where this job sits and how this role will contribute to the team’s delivery of critical function.
- Serve as a Manager-level Clinical Data Scientist within Data & Quantitative Sciences, applying statistical, data science, and analytical methods to support clinical development programs.
- Contribute to cross-functional study teams by delivering analysis-ready data, quantitative analyses, visualizations, and interpretation summaries for assigned studies or workstreams.
- Support fit-for-purpose statistical, data science, and advanced analytics activities under the direction of study and functional leadership.
- Collaborate with cross-functional team members to support high-quality, traceable, analysis-ready, and submission-ready data.
- Apply modern clinical data science practices, including automation, reusable analytics workflows, and approved AI/ML-enabled approaches, while maintaining scientific rigor, regulatory awareness, and patient-focused decision making.
Accountabilities:
Primary duties and responsibilities; essential functions only.
- Execute clinical data science activities for assigned studies or workstreams, ensuring timely delivery of high-quality analyses, data review, and quantitative insights that support study objectives.
- Perform exploratory analyses, data visualization, and quantitative assessments using clinical trial, biomarker, external, and real-world data sources.
- Translate scientific and clinical questions into analysis-ready datasets, analysis specifications, and reproducible analytical workflows with guidance from senior team members.
- Support integrated data review activities by identifying data trends, inconsistencies, and potential risks requiring further investigation.
- Apply established statistical, machine learning, simulation, and visualization methods to support interpretation of study results and development decisions.
- Review and contribute to outputs produced by internal teams and external partners, ensuring adherence to established standards, processes, and quality expectations.
- Communicate risks related to data quality, analytical assumptions, timelines, and quantitative outputs to study leadership and functional stakeholders.
- Contribute to continuous improvement efforts through automation, reusable code, standard methodologies, and adoption of approved technologies and workflows.
Education & Competencies (Technical and Behavioral):
Essential and desirable education and competency requirements to perform the primary responsibilities of the job.
Education / Experience
- PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or related field; or MS with 3+ years of relevant experience. Equivalent combinations should be reviewed with HR.
- Experience contributing to quantitative analyses and data science activities within pharmaceutical, biotechnology, healthcare research, or other regulated clinical development environments.
- Demonstrated ability to support clinical development decisions through quantitative analysis, data interpretation, and clear communication of evidence.
- Experience working effectively on cross-functional study teams and collaborating across functional disciplines to achieve study objectives.
- Experience working with clinical trial data and at least one additional data type such as biomarker, real-world, external, imaging, digital health, or other high-dimensional data sources.
Highest-priority Technical Skills
- Working knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.
- Solid foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and communication of uncertainty.
- Hands-on proficiency in R and/or Python, with working knowledge of SAS and SQL; ability to develop and support reproducible analyses, code quality, version control, and validated workflows.
- Working knowledge of CDISC standards, including SDTM, ADaM, controlled terminology, Define-XML concepts, and submission-oriented data expectations.
- Ability to integrate, analyze, and interpret diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or high-dimensional data as appropriate to assigned studies.
- Practical understanding of AI/ML and advanced analytics in regulated clinical development, including model development, validation, documentation, assumptions, bias considerations, and fit-for-purpose deployment.
- Awareness of FDA, EMA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
- Ability to create clear analysis specifications, visualization approaches, documentation, and interpretation summaries suitable for scientific, operational, and study-team audiences.
- Familiarity with modern data platforms, reusable analytics workflows, automation, metadata-driven processes, and governed data standards.
Behavioral Competencies
- Communicates quantitative findings clearly to scientific, operational, technical, and study-team audiences.
- Builds effective working relationships across study teams and functional partners.
- Demonstrates technical credibility, sound judgment, and collaborative problem-solving skills.
- Balances scientific rigor, quality, and timely delivery while proactively communicating risks and issues.
- Demonstrates accountability for assigned deliverables and commitment to reproducible, traceable, high-quality work.
- Embraces continuous learning and adoption of innovative analytical methods, automation, and AI-enabled approaches.