Head of Analytical Data & Statistics, R&D
Takeda · Fujisawa
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.