Opportunity for Clinical Data Scientist to Join Takeda

Takeda has been translating science into breakthrough medicines for 240 years. Every step of the way, our teams have worked together to tackle some of the most challenging problems in drug discovery and development. Today, we’re a driving force behind innovative therapies that make a lasting difference to millions of patients around the world. In R&D, all of our history and potential comes together in an environment that welcomes diversity of thought and amplifies every voice. Working closely with colleagues, you’ll play a key role in bringing our rich pipeline of products forward to help patients.

Post : Manager, Clinical Data Scientist

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.

Candidate Profile
• 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.

Additional Information
Experience : 3+ years
Qualification : PhD or MS
Location : Bengaluru
Industry Type : Pharma/ Healthcare/ Clinical research
Functional Area : Research and Development
End Date : 30th September 2026