Associate Director, Clinical Data Scientist

Overview

Serve as an Associate Director-level clinical data science leader within Data & Quantitative Sciences, translating complex clinical, biomarker, and external data into actionable evidence for clinical development decisions. Lead statistical, data science, and advanced analytics approaches across assigned studies, assets, or specialty areas.

What you'll do

• Lead clinical data science strategy and delivery for one or more studies, assets, or capability areas.
• Design and/or execute quantitative analyses using clinical trial data, biomarkers, real-world data, external data, and other relevant sources.
• Apply statistical, machine learning, simulation, and visualization methods for 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.
• Partner with Clinical Pharmacology, PSPV, Translational Sciences, Clinical Data Management, Regulatory, and platform teams on CDISC, submission, and quantitative decision-making needs.
• Provide scientific and technical oversight of internal and external delivery partners, including analysis plans, specifications, code, outputs, data visualization, and findings.
• Identify, communicate, and mitigate risks related to data quality, analytic assumptions, vendor delivery, timelines, reproducibility, and regulatory acceptability.
• Drive continuous improvement through reusable code, standards, training, mentoring, automation, AI-enabled workflow improvements, and industry best practices.
• Mentor junior colleagues or delivery partners in clinical data science methods, reproducible practices, technical problem solving, and communication of quantitative insights.
• Partner cross-functionally with Clinical, Clinical Pharmacology, PSPV, Clinical Data Management, Translational Sciences, Regulatory, Clinical Operations, and external partners.

What you'll need

• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or a related field with 5+ years of relevant experience; or MS with 8+ years of relevant experience.
• Significant experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment.
• 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.
• Advanced knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and 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.
• 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 analytics in regulated clinical development, including model development, validation, documentation, bias/assumption assessment, and fit-for-purpose deployment.
• Knowledge of FDA, EMA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
• Ability to communicate complex quantitative findings clearly to scientific, operational, technical, and senior leadership audiences.
• Ability to influence across functions without relying on direct authority and build trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor stakeholders.
• Ability to balance scientific rigor, speed, quality, and pragmatic delivery, and proactively escalate risks with options and recommendations.
• Demonstrated enterprise mindset, curiosity, continuous improvement, commitment to developing others, and interest in advancing modern clinical data science capabilities.

Details

• Location: Bengaluru, Research and Development, India.
• Employment type: Full time.
• Worker type: Employee.
• Worker sub-type: Regular.
• Flexible work environment, including hybrid working.