Associate Director, Clinical Data Scientist (India)

Takeda Pharmaceutical; India; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Takeda Pharmaceutical seeks an Associate Director-level clinical data science leader to translate complex clinical, biomarker, and external data into actionable evidence for development decisions.

You will lead fit-for-purpose statistical, data science, and advanced analytics approaches across studies, partnering with Clinical, Regulatory, PSPV, Clinical Data Management, and Translational Sciences to deliver high-quality, decision-ready insights.

Qualifications

• PhD or MS with extensive experience in clinical data science and statistics.
• Experience influencing cross-functional decisions in clinical development.
• Proven ability to mentor junior colleagues and deliver regulatory-ready analyses.
• Strong knowledge of CDISC standards and data management practices.

Responsibilities

• Lead analyses using clinical trial, biomarker, and external data sources.
• Develop predictive models and simulations to inform development decisions.
• Ensure data lineage, quality, and traceability for regulated submissions.
• Provide scientific oversight of external delivery partners.

Skills

Clinical trial design

Drug development

Biomarkers

Data interpretation

Analytics for decision making

R

Python

SAS

SQL

CDISC SDTM

CDISC ADaM

AI/ML basics

Reproducible workflows

Education

PhD in statistics/biostatistics/data science

MS in statistics/biostatistics/biomedical engineering or related field

Tools

R

Python

SAS

SQL

Job description

Job Description

• Serve as an Associate Director-level clinical data science leader within Data & Quantitative Sciences, translating complex clinical, biomarker, and external data into actionable evidence that informs clinical development decisions.
• Lead fit-for-purpose statistical, data science, and advanced analytics approaches across assigned studies, assets, or specialty areas, including exploratory analysis, predictive modeling, simulation, and integrated data review.
• Partner cross-functionally with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, and external partners to ensure high-quality, traceable, analysis and submission-ready data and decision-ready insights.
• Advance modern ways of working by applying AI/ML, automation, reusable analytics workflows, and governed data standards while maintaining scientific rigor, regulatory awareness, and patient-focused decision making.

Accountabilities:

• Lead clinical data science strategy and delivery for one or more studies, assets, or capability areas, ensuring alignment with development objectives, timelines, quality expectations, and stakeholder needs.
• Design and/or execute quantitative analyses using clinical trial data, biomarkers, real-world data, external data, and other relevant sources to generate interpretable insights for study teams and governance forums.
• Apply appropriate statistical, machine learning, simulation, and visualization methods to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, and evidence generation.
• Define requirements for model-ready datasets and analytics-ready data flows, including variable derivations, data quality expectations, lineage, traceability, metadata, and documentation sufficient for regulated clinical development use.
• Partner with Clinical Pharmacology PSPV, Translational Sciences, Clinical Data Management, Regulatory, and platform teams to ensure that CDISC, submission, and downstream quantitative decision-making needs are built into study setup, data review, and reporting processes.
• Provide scientific and technical oversight of internal and external delivery partners, including review of analysis plans, specifications, code, outputs, data visualization, and interpretation of findings.
• Identify, communicate, and mitigate risks related to data quality, analytic assumptions, vendor delivery, timelines, reproducibility, and regulatory acceptability of data science outputs.
• Drive continuous improvement in clinical data science practices through reusable code, standards, training, mentoring, automation, AI-enabled workflow improvements, and adoption of industry best practices.
• Mentor junior colleagues or delivery partners in clinical data science methods, reproducible analytic practices, technical problem solving, and effective communication of quantitative insights.

Key Requirements:

• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or related field with 5+ years of relevant experience; or MS with 8+ years of relevant experience. Equivalent combinations should be reviewed with HR.
• Significant experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment, with demonstrated ability to influence cross-functional decisions at study, asset, or functional level.
• Experience providing technical leadership, matrix leadership, vendor oversight, and/or mentorship of junior colleagues or delivery partners.

Highest-priority Technical Skills

• Advanced 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 uncertainty communication.
• Hands-on proficiency in R and/or Python, with working knowledge of SAS and SQL; ability to review and guide 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.
• Experience integrating and interpreting diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or other high-dimensional data as appropriate to the portfolio.
• Practical understanding of AI/ML and advanced .