Director, Clinical Data Scientist Bengaluru, Karnataka Category: Data Sciences

Job description

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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: • 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 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. • 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. • 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. • 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. • 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. • 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. • 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. • 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. • 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. Education & Competencies (Technical and Behavioral): 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. • Str