Associate Director, Clinical Data Scientist - Statistics
Description
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Job Description
Objective / Purpose
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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.
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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.
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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.
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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
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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.
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Apply appropriate statistical, machine learning, simulation, and visualization methods to support patient-level prediction, endpoint interpretation, risk assessment, scenario planning, and evidence generation.
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Perform end-to-end data analyses, from hypotheses formulation, experimental design, writing analysis plans, data cleaning, executing analysis, and preparing reports and documentation.
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Provide or identify internal and external statistical expertise and capacity to support development activities.
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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.
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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.
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Identify, communicate, and mitigate risks related to data quality, analytic assumptions, vendor delivery, timelines, reproducibility, and regulatory acceptability of data science outputs.
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Assess, communicate and propose solutions for internal, external resource and/or quality issues that may impact deliverables/timeline at the program level.
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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.
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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.
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Mentor junior colleagues or delivery partners in clinical data science methods, reproducible analytic practices, technical problem solving, and effective communication of quantitative insights.
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Increase the external recognition of Takeda’s data science work by participating in conferences, publishing work and developing external collaborations.
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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
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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.
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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.
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Experience providing technical leadership, matrix leadership, vendor oversight, and/or mentorship of junior colleagues or delivery partners.
Highest-priority Technical Skills
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Advanced knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making.
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Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and uncertainty communication.
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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.
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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.
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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.
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Ability to work independently on complicated datasets, including all aspects of data analysis (data cleaning, algorithm development, statistical analysis, and documentation).
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Working knowledge of CDISC standards, including SDTM, ADaM, controlled terminology, Define-XML concepts, and submission-oriented data expectations.
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Knowledge of FDA, EMA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data and quantitative deliverables.
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A working knowledge of UNIX operating systems is preferred, ideally with experience in high-performance computing environments.
Behavioral Competencies
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Communicates complex quantitative findings clearly to scientific, operational, technical, and senior leadership audiences.
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Influences across functions without relying on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor stakeholders.
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Balances scientific rigor, speed, quality, and pragmatic delivery; proactively escalates risks with options and recommendations.
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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
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Flexible work environment, including hybrid working
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Comprehensive Healthcare Insurance Plans for self, spouse, and children
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Group Term Life Insurance and Group Accident Insurance programs
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Health & Wellness programs including annual health screening, weekly health sessions for employees.
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Employee Assistance Program
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5 days of leave every year for Voluntary Service in addition to Humanitarian Leaves
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Broad Variety of learning platforms
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Diversity, Equity, and Inclusion Programs
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No Meeting Days
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Reimbursements – Home Internet & Mobile Phone
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Employee Referral Program
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Leaves – Paternity Leave (4 Weeks) , Maternity Leave (up to 26 weeks), Bereavement Leave (5 days)
About ICC in Takeda
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