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

Worker Type

Worker Sub-Type

Time Type