Senior Manager, Clinical Data Scientist - Statistics

Takeda; Bengaluru; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

Takeda in Bengaluru, India seeks a Senior Manager-level Clinical Data Scientist to drive analysis-ready data, execute quantitative analyses, and support decision-making across development programs.

You will collaborate with clinical, regulatory, and data science teams to deliver scalable, traceable analytics and modern data workflows, while adhering to GxP standards.

Qualifications

• PhD or MS with extensive experience in quantitative clinical data science.
• Experience supporting quantitative analyses in pharma/biotech settings.
• Ability to influence clinical development decisions through data.
• Experience with cross-functional study teams and data types.

Responsibilities

• Serve as a Senior Manager-level Clinical Data Scientist within Data & Quantitative Sciences.
• Deliver analyses and insights to support decisions in development programs.
• Translate questions into reproducible analytical workflows and datasets.
• Collaborate with Clinical/Data Management/Pharmacology to ensure traceable data.
• Apply AI/ML approaches with regulatory awareness and rigor.

Skills

Clinical data science

Statistical analysis

Cross-functional collaboration

AI/ML in regulated settings

Data visualization

Communication of results

Reproducible analytics workflows

Project delivery

Education

PhD in statistics/biostatistics/data science or related field

Tools

R

Python

SAS

SQL

Job description

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 Senior Manager-level Clinical Data Scientist within Data & Quantitative Sciences, applying statistical, data science, and analytical methods to support clinical development programs.


• Partner with cross-functional study teams to deliver analysis-ready data, perform quantitative analyses, interpret results, and generate decision-support insights.


• Deliver fit-for-purpose statistical, data science, and advanced analytics activities for assigned studies and study-level workstreams.


• Collaborate with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, Statistical Programming, and external partners to support high-quality, traceable, analysis-ready, and submission-ready data.


• Apply modern clinical data science practices, including automation, reusable analytics workflows, and AI/ML-enabled approaches, while maintaining scientific rigor, regulatory awareness, and patient-focused decision making.

_Accountabilities: _

_Primary duties and responsibilities; essential functions only.

• Perform exploratory analyses, data visualization, and quantitative assessments using clinical trial, biomarker, external, and real-world data sources.


• Apply established statistical, machine learning, simulation, and visualization methods to support interpretation of study results and development decisions.


• Translate scientific and clinical questions into analysis-ready datasets, specifications, and reproducible analytical workflows.


• Execute clinical data science activities for assigned studies, ensuring timely delivery of high-quality analyses, data review, and quantitative insights that support study objectives.


• Support integrated data review activities by identifying data trends, inconsistencies, and potential risks requiring further investigation.


• Contribute to the review of analysis outputs, visualizations, and technical documentation to ensure quality, traceability, and reproducibility of deliverables.


• Review and contribute to analysis outputs produced by internal teams and external partners, ensuring quality and adherence to established standards and processes.


• Identify and communicate risks related to data quality, analytical assumptions, timelines, and quantitative outputs to functional stakeholders.


• Collaborate with Clinical Data Management, Clinical Pharmacology, PSPV, Clinical Operations, and Translational Sciences to support study objectives and evidence generation.


• Share technical expertise and support onboarding and development of less experienced team member


• Contribute to departmental standards, process improvements, and technology adoption initiatives as assigned.


• 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 with 3+ years of relevant experience; or MS with 6+ years of relevant experience. Equivalent combinations should be reviewed with HR.


• Experience supporting quantitative analyses and data science activities within pharmaceutical, biotechnology, healthcare research, or other regulated clinical development environments.


• Demonstrated ability to contribute to clinical development decisions through quantitative analysis, data interpretation, and effective 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 one or more additional data types such as biomarker, real-world, external, imaging, digital health, or other high-dimensional data sources.

Highest-priority Technical Skills

• Strong knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.


• Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and communication of uncertainty.


• Experience integrating, analyzing, and interpreting 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.


• Hands-on proficiency in R and/or Python, with working knowledge of SAS and SQL; ability to develop, review, and support reproducible analyses, code quality, version control, and validated workflows.


• Ability to develop clear analysis specifications, visualization approaches, documentation, and interpretation summaries suitable for scientific, operational, and study-team audiences.


• Working knowledge of CDISC standards, including SDTM, ADaM, controlled terminology, Define-XML concepts, and submission-oriented data expectations.


• Knowledge of FDA, EMA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.


• 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 leadership audiences.


• Builds effective working relationships across study teams and functional partners.


• 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.


• Demonstrates strong technical credibility, sound judgment, and collaborative problem-solving skills.


• 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