Manager, Clinical Data Scientist

Takeda; Bengaluru; Presencial; INR 1.800.000 - 2.400.000; Jornada completa

Descripción de la vacante

Takeda seeks a Manager-level Clinical Data Scientist to apply statistical and data science methods across clinical development programs. You will deliver analysis-ready data, quantitative analyses, and interpretable summaries to support study decisions, with a focus on automation, reusable workflows, and approved AI/ML approaches.

You will work with cross-functional teams to ensure data quality, traceability, and submission-ready outputs, while maintaining scientific rigor and patient-centric

Formación

• PhD/MS with relevant experience in regulated clinical development environments.
• Experience contributing to quantitative analyses and data science activities.
• Ability to support clinical development decisions through quantitative analysis and communication.

Responsabilidades

• Execute clinical data science activities to deliver analyses and insights.
• Translate clinical questions into analysis-ready datasets and reproducible workflows.
• Collaborate with cross-functional study teams to ensure high-quality data and outputs.
• Apply AI/ML and advanced analytics with regulatory awareness.

Conocimientos

R

Python

SAS

SQL

CDISC SDTM/ADaM

AI/ML basics

Regulatory awareness

Educación

PhD in statistics/biostatistics/data science

MS with 3+ years experience

Herramientas

SAS

SQL

Python

Descripción del empleo

Job Description

• 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

• 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)

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.

Locations

IND - Bengaluru - Research and Development

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time