Director, Clinical Data Scientist - Statistics

Takeda; Bengaluru; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

Takeda in Bengaluru, India seeks a Director-level clinical data science leader to shape strategy, deliver scientific insights, and drive submission readiness across studies. You will lead a team of clinical data scientists and matrix contributors, ensuring high-quality analyses and regulatory-aligned outputs.

You will translate diverse data sources—trial, biomarker, real-world—into evidence for development decisions, collaborate with Regulatory, Clinical Operations, and Data Management, and

Qualifications

• PhD in a quantitative field with 8+ years in clinical data science.
• Proven track record in regulatory submissions and health authority interactions.
• Experience managing teams in a matrix environment.

Responsibilities

• Lead and develop a team of clinical data scientists for multiple studies.
• Translate clinical and real-world data into evidence for regulatory submissions.
• Oversee data quality, analytics readiness, and inspection readiness across authorities.
• Partner cross-functionally to ensure CDISC, submission readiness, and governance.

Skills

Clinical data science

Statistics

AI/ML

R/Python

CDISC SDTM

ADaM

Regulatory submissions

Education

PhD or MS in a quantitative field

Tools

R

Python

SAS

SQL

Job description

Job Description

_ Objective / Purpose: _

• Serve as a Director-level clinical data science leader and people manager within Data & Quantitative Sciences, accountable for advancing clinical data science strategy, delivery excellence, submission readiness, and team capability across assigned studies, assets, or specialty areas.
• Lead and develop a team of clinical data scientists and/or matrixed delivery contributors, setting clear priorities, enabling high-quality execution, and fostering a culture of scientific rigor, accountability, inclusion, collaboration, inspection readiness, and continuous improvement.
• Translate complex clinical, biomarker, real-world, external, and high-dimensional data into actionable evidence that informs clinical development strategy, health authority interactions, regulatory submissions, governance decisions, and patient-focused decision making.
• Shape fit-for-purpose statistical, data science, and advanced analytics approaches across a portfolio, including exploratory analysis, predictive modeling, simulation, integrated data review, automation, and AI/ML-enabled methods suitable for regulated clinical development and submission use.
• Partner cross-functionally with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, Statistical Programming, technology/platform teams, and external partners to ensure high-quality, traceable, analysis-ready, health-authority-ready data and decision-ready insights across multiple regulatory jurisdictions.

_ Accountabilities: _

• Lead design and interpretation of quantitative analyses using clinical trial data, biomarkers, real-world data, external data, and other relevant sources to generate evidence for study teams, asset teams, governance forums, health authority engagements, and regulatory submission packages.
• Guide application of statistical, machine learning, simulation, and visualization methods to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, integrated data review, evidence generation, and submission-oriented interpretation.
• Set clinical data science direction and delivery priorities for assigned studies, assets, portfolio areas, or capability domains, ensuring alignment with development objectives, functional strategy, global regulatory strategy, submission timelines, quality expectations, and stakeholder needs.
• Provide scientific, technical, operational, and submission-readiness oversight of internal teams and external delivery partners, including review of analysis plans, specifications, code, outputs, data visualization, narratives, documentation, and interpretation of findings.
• Identify, communicate, and mitigate risks related to data quality, analytic assumptions, vendor delivery, resource capacity, timelines, reproducibility, inspection readiness, and regulatory acceptability of data science outputs across multiple health authorities.
• Oversee resource planning and delivery execution across assigned work, balancing portfolio priorities, capacity, external partner contributions, submission milestones, and risk mitigation to ensure high-quality and timely outputs.
• Partner with Clinical Pharmacology, PSPV, Translational Sciences, Clinical Data Management, Regulatory, Statistical Programming, and platform teams to ensure CDISC, submission, and downstream quantitative decision-making needs are reflected in study setup, data review, analysis planning, reporting, and health authority response processes.
• Define expectations for model-ready datasets and analytics-ready data flows, including variable derivations, data quality expectations, lineage, traceability, metadata, documentation, and fit-for-purpose use in regulated clinical development, inspections, and submissions.
• Support preparation for regulatory interactions and submissions by ensuring analytical outputs are well documented, traceable, reproducible, appropriately governed, and aligned with expectations from FDA, EMA, PMDA, NMPA, MHRA, and other relevant health authorities, as applicable.
• Provide people leadership for direct reports, including goal setting, performance management input, coaching, career development, workload prioritization, engagement, and support for talent growth and retention.
• Build team capability by mentoring and developing clinical data scientists, creating opportunities for technical growth, strengthening reproducible analytics practices, and promoting effective communication of quantitative insights in study, governance, and regulatory contexts.
• Represent Clinical Data Science in cross-functional and regulatory-facing forums, influencing stakeholders and ensuring quantitative insights are clearly connected to clinical development questions, submission strategy, health authority expectations, decisions, and patient impact.
• Drive continuous improvement in clinical data science practices through reusable code, standards, training, automation, AI-enabled workflow improvements, governed data standards, and adoption of industry best practices for submission-ready delivery.

Education / Experience

• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or related field with 8+ years of relevant experience; or MS with 12+ years of relevant experience. Equivalent combinations should be reviewed with HR.
• Extensive experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment, with demonstrated ability to influence cross-functional decisions at study, asset, portfolio, or functional level.
• Demonstrated experience contributing to regulatory submissions, health authority interactions, inspection readiness, and submission-oriented analysis, documentation, traceability, and response activities across multiple regulatory agencies or global health authorities.
• Demonstrated experience as a people manager or formal team leader, including coaching, performance input, talent development, workload prioritization, and support for employee engagement and growth.
• Experience providing technical leadership, matrix leadership, vendor oversight, and mentorship across cross-functional, geographically distributed, or externally supported delivery models.
• Track record of advancing analytical strategy, standards, automation, AI/ML-enabled approaches, or modern data science practices in a regulated clinical development and submission environment.

Highest-priority Technical Skills

• Expert knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making, regulatory strategy, and submission support.
• Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and uncertainty communication for scientific, governance, and health authority audiences.
• 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 and regulatory context.
• Practical understanding of AI/ML and advanced analytics in regulated clinical development, including model development, validation, documentation, bias/assumption assessment, governance, explainability, and fit-for-purpose deployment in regulatory-relevant settings.
• Hands-on fluency in R and/or Python, with working knowledge of SAS and SQL; ability to guide reproducible analyses, code quality, version control, reusable workflows, validated delivery practices, and inspection-ready documentation.
• Strong working knowledge of CDISC standards and submission expectations, including SDTM, ADaM, controlled terminology, Define-XML concepts, reviewer guides, traceability, data lineage, and submission-oriented data package requirements.
• Deep knowledge of FDA, EMA, PMDA, NMPA, MHRA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data, quantitative deliverables, and global submission packages.
• Ability to establish analytical standards, technical expectations, documentation practices, quality review approaches, and submission-readiness controls that enable scalable and inspection-ready delivery across multiple health authorities.

People Leadership & Behavioral Competencies

• Communicates complex quantitative findings clearly to scientific, operational, technical, executive, senior leadership, and health authority-facing audiences.
• Influences across functions without relying solely on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor stakeholders.
• Balances scientific rigor, speed, quality, resource capacity, regulatory risk, submission timelines, and pragmatic delivery; proactively escalates risks with options and recommendations.
• Demonstrates curiosity, continuous improvement, sound judgment, and commitment to advancing modern clinical data science capabilities, developing others, and maintaining submission-ready standards.
• Leads with clarity, accountability, inclusion, and enterprise mindset; creates an environment where team members can deliver, grow, collaborate effectively, and uphold regulatory-quality expectations.
• Coaches and develops direct reports and matrixed contributors, providing actionable feedback, supporting career growth, and building future technical, regulatory, submission, and leadership capability.

Locations

IND - Bengaluru - Research and Development

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time