Head of Analytical Data & Statistics, R&D

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 OBJECTIVES/PURPOSE: Establish and continuously improve statistical and analytical standards, methods, and governance for the organization. Ensure robust data architecture, collection, validation, and quality control processes to maintain data integrity and traceability. Embed analytics into study/assay design and milestone decisions through close partnership with cross-functional teams. Deliver clear, actionable insights and visual reporting that inform program strategy, prioritization, and resource allocation. Drive adoption of modern analytical tools and data automation to improve speed, reproducibility, and scalability (e.g., AI-assisted generation and modelling where appropriate). Maintain compliance with applicable regulations and industry best practices for statistical analysis and data management. Develop team capability through hiring, coaching, training, and performance development. ACCOUNTABILITIES: Lead and manage the Analytical Data and Statistics team to deliver high-quality data analysis and reporting. Develop and implement advanced statistical methods and analytical frameworks to support biotechnology research and development. Collaborate with cross-functional teams to integrate data analytics into experimental design and decision-making processes. Oversee data collection and architecture, validation, and quality control procedures to ensure data integrity and compliance with regulatory requirements. Drive innovation in data analytics by evaluating and adopting new tools, technologies, and methodologies. Present analytical findings and insights to senior leadership to guide strategic planning and project prioritization. Ensure compliance with industry standards, guidelines, and best practices in statistical analysis and data management. Mentor and train team members to build expertise in statistical techniques and data analytics. DIMENSIONS AND ASPECTS: Technical/Functional (Line) Expertise: Deep expertise in applied statistics for Analytical Development (e.g., DoE, regression/multivariate methods, variance components, measurement system analysis, stability trending, and comparability assessments). Strong command of analytical data types and workflows (chromatography, electrophoresis, mass spectrometry, potency/bioassays) and the key sources of variability that drive method performance. Experience defining and governing data standards, statistical analysis plans, and fit-for-purpose acceptance criteria for method development, qualification/validation, and lifecycle management. Proficiency with modern analytics tooling (R/Python/SAS or equivalent), reproducible workflows (version control, code review), and visualization/dashboarding practices. Understanding of GxP-relevant data integrity and compliance expectations (ALCOA+, audit trails, validation of computerized systems) and how they apply to analytical data and reporting. Capability to translate complex analyses into decision-ready narratives for technical and non-technical stakeholders; strong scientific judgment on uncertainty and risk. Familiarity with analytical informatics ecosystems (LIMS/ELN/CDS and data lakes) and data integration/automation approaches to enable scalable insights. Leadership: Sets vision and strategy for how analytical data and statistics enable portfolio decisions; aligns priorities, resourcing, and roadmap to AD and enterprise objectives. Leads, coaches, and develops a multidisciplinary team; sets expectations, builds capability, and fosters a culture of quality and continuous improvement. Influences cross-functionally to embed data-driven ways of working; establishes governance/operating cadence, leads change adoption for standards and tools, and communicates complex analyses clearly with risk and uncertainty framing. Decision-making and Autonomy: Owns prioritization of the Analytical Data & Statistics portfolio and allocation of team capacity; sets delivery commitments, timelines, and quality expectations. Defines and approves statistical approaches, analysis plans, and reporting standards for AD studies and method lifecycle activities; serves as the escalation point for complex technical/statistical issues. Makes decisions on data governance (data standards, metadata, access/retention, and validation requirements) and ensures alignment with GxP expectations and internal policies. Recommends go/no-go and risk-based options at key program milestones by translating uncertainty into clear decision trade-offs (speed, cost, quality, and compliance). Selects and champions analytics tools and digital solutions within delegated authority; escalates investments with material budget, compliance, or enterprise architecture impact. Makes decisions on data governance (data standards, metadata, access/retention, and validation requirements) and ensures alignment with GxP expectations and internal policies. Recommends go/no-go and risk-based options at key program milestones by translating uncertainty into clear decision trade-offs (speed, cost, quality, and compliance). Selects and champions analytics tools and digital solutions within delegated authority; escalates investments with material budget, compliance, or enterprise architecture impact. Interaction: Internal AD teams: Daily partnership with Analytical Development functional leads and project teams to frame analytical questions, shape study/assay designs, and interpret results for milestone decisions. Internal Quality/Regulatory: Routine engagement with QA/Quality Systems and Regulatory CMC/Technical Writing to ensure data integrity, inspection readiness, and submission-ready statistical rationales and presentations. Internal & External Digital/Operations: Close collaboration with IT/Lab Informatics/data platform teams (LIMS/ELN/CDS, data lakes) plus MSAT/Manufacturing and DD&T to enable validated solutions. Innovation: Identifies and pilots novel statistical and analytics approaches (e.g., advanced modeling, multivariate methods, and AI-assisted analysis where appropriate) to improve decision quality and speed. Drives standardization and reuse (templates, libraries, validated workflows) to increase reproducibility, reduce rework, and enable scale across programs and sites. Promotes knowledge sharing and technical excellence through communities of practice, peer review, training, and documentation of best practices. Balances innovation with risk management: evaluates suitability, validation needs, and compliance impacts before broad deployment; defines guardrails for responsible use of new tools. Continuously scans external best practices (literature, conferences, industry consortia) and translates learnings into pragmatic improvements to methods, standards, and ways of working. Complexity: Works across a diverse AD portfolio (modalities, assays, platforms) and must standardize approaches while tailoring to program-specific questions and timelines. Integrates high-dimensional, heterogeneous data from multiple systems with variable data quality/metadata maturity, requiring strong governance and pragmatic solutioning. Balances speed and innovation with compliance expectations in a matrixed environment; makes high-impact recommendations under uncertainty (assumptions, limited sample sizes, evolving methods). EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS: Advanced degree (PhD or Master’s) in statistics, biostatistics, data science, bioinformatics, or a related field. Extensive experience in statistical analysis and data management within the biotechnology or pharmaceutical industry. Experience with analytical development in biologics and/or small molecules is preferred. Strong knowledge of experimental design including DoE, statistical modeling, data visualization techniques, and AI-based tools. Proficiency in statistical software such as R, SAS, Python, or similar tools. Demonstrated leadership skills with experience managing high-performing analytics teams. Excellent communication and interpersonal abilities to collaborate effectively with diverse stakeholders. Familiarity with regulatory requirements and guidelines relevant to biotechnology data and statistics. Ability to translate complex data into clear, actionable insights for decision-making. Strategic thinking: innovative, pragmatic, priority-focused; able to assess current and future scenarios. Work style: structured, goal-oriented, highly motivated. Language: fluent English (Japanese is a plus). Takeda Compensation and Benefits Summary: Allowances: Commutation, Housing, Overtime Work etc. Salary Increase: Annually, Bonus Payment: Twice a year Working Hours: Headquarters (Osaka/ Tokyo) 9:00-17:30, Production Sites (Osaka/ Yamaguchi) 8:00-16:45, (Narita) 8:30-17:15, Research Site (Kanagawa) 9:00-17:45 Holidays: Saturdays, Sundays, National Holidays, May Day, Year-End Holidays etc. (approx. 123 days in a year) Paid Leaves: Annual Paid Leave, Special Paid Leave, Sick Leave, Family Support Leave, Maternity Leave, Childcare Leave, Family Nursing Leave. Flexible Work Styles: Flextime, Telework Benefits: Social Insurance, Retirement and Corporate Pension, Employee Stock Ownership Program, etc. Important Notice concerning working conditions: It is possible the job scope may change at the company’s discretion. It is possible the department and workplace may change at the company’s discretion. Locations Fujisawa, Japan Worker Type Employee Worker Sub-Type Regular Time Type Full time