Associate Director, Clinical Data Scientist - Statistics

By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use . I further attest that all information I submit in my employment application is true to the best of my knowledge. 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&#43; years of relevant experience; or MS with 8&#43; 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:<