Associate Director, Clinical Data Scientist - Statistics

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Job Description

Objective / Purpose:

• 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:

• 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.

• Perform end-to-end data analyses, from hypotheses formulation, experimental design, writing analysis plans, data cleaning, executing analysis, and preparing reports and documentation.

• Provide or identify internal and external statistical expertise and capacity to support development activities.

• 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.

• 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.

• Assess, communicate and propose solutions for internal, external resource and/or quality issues that may impact deliverables/timeline at the program level.

• 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.

• 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.

• Mentor junior colleagues or delivery partners in clinical data science methods, reproducible analytic practices, technical problem solving, and effective communication of quantitative insights.

• Increase the external recognition of Takeda’s data science work by participating in conferences, publishing work and developing external collaborations.

• 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.

Education & Competencies (Technical and Behavioral):

Education / Experience

• PhD in statistics, biostatistics, data science, applied mathematics, physics, 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.

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

• 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 analytics in regulated clinical development, including model development, validation, documentation, bias/assumption assessment, and fit-for-purpose deployment.

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

• Ability to work independently on complicated datasets, including all aspects of data analysis (data cleaning, algorithm development, statistical analysis, and documentation).

• 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.

• A working knowledge of UNIX operating systems is preferred, ideally with experience in high-performance computing environments.

Behavioral Competencies

• Communicates complex quantitative findings clearly to scientific, operational, technical, and senior leadership audiences.

• Influences across functions without relying on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor stakeholders.

• Balances scientific rigor, speed, quality, and pragmatic delivery; proactively escalates risks with options and recommendations.

• Demonstrates enterprise mindset, curiosity, continuous improvement, and commitment to developing others and advancing modern clinical data science capabilities.

Benefits

It is our priority to provide competitive compensation and a benefit package that bridges your personal life with your professional career. Amongst our benefits are:

Competitive Salary + Performance Annual Bonus

• Flexible work environment, including hybrid working

• Comprehensive Healthcare Insurance Plans for self, spouse, and children

• Group Term Life Insurance and Group Accident Insurance programs

• Health & Wellness programs including annual health screening, weekly health sessions for employees.

• Employee Assistance Program

• 5 days of leave every year for Voluntary Service in addition to Humanitarian Leaves

• Broad Variety of learning platforms 

• Diversity, Equity, and Inclusion Programs

• No Meeting Days

• Reimbursements – Home Internet & Mobile Phone

• Employee Referral Program

• Leaves – Paternity Leave (4 Weeks) , Maternity Leave (up to 26 weeks), Bereavement Leave (5 days)

About ICC in Takeda

• Takeda is leading a digital revolution. We’re not just transforming our company; we’re improving the lives of millions of patients who rely on our medicines every day.

• As an organization, we are committed to our cloud-driven business transformation and believe the ICCs are the catalysts of change for our global organization.

Locations

IND - Bengaluru

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time

Locations

IND - Bengaluru - Research and Development

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time