Principal Data Scientist/Non-Line Manager, Experimental Medicine Japan D&A Full-time

Job Title

Supports the complete clinical/pharmaceutical drug lifecycle process (research, development, market access, and market supply) through strategic planning and execution, data transformation, descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Works with data from clinical trials, clinical registries, and real-world databases. Provides analytics tools, data outputs, scientific insights and inference. May act as an ExpMED Product Owner up to the substance/asset level and represent ExpMED on data science-related matters.

Key Accountabilities

Responsible for leading and overseeing design, transformation, analysis and reporting for complex Phase I-IV clinical trials. Supporting complex international projects. Leading analysis of registry and real-world data. Delivering data science solutions aligned with specific project and asset needs.

Success Measures

Quality deliverables, timeline adherence, feedback from development teams, Product Owners and capability managers.

Innovation & Scientific Advancement

Stay current on developments in data science both within and outside BI. Convert insights into new data science approaches supporting discovery, clinical development, regulatory registration, manufacturing, and commercialization.

Success Measures

Quality of innovative solutions, adoption of new processes and tools, stakeholder feedback.

Data Storytelling & Communication

Present compelling, validated stories based on complex data science findings. Communicate effectively with scientific and non-scientific stakeholders.

Success Measures

Quality and frequency of presentations, audience understanding and feedback.

Compliance & Data Quality

Ensure data transformation and analysis specifications are complete, accurate, SOP-compliant, and GxP-compliant.

Success Measures

Regulatory acceptance, quality of specifications.

Coaching & Knowledge Sharing

Guide and lead colleagues. Support internal customers and external partners. Promote knowledge sharing within the Clinical Data Science community.

Success Measures

Feedback from colleagues, increased knowledge sharing and capability development.

Cross-Functional Leadership

Participate in BI cross-functional working groups. Lead One Human Pharma internal working groups. Participate in external industry working groups. Drive relevant data science initiatives.

Success Measures

Quality of leadership, business impact of working group outcomes, feedback from Global Product Owners and Product Owners.

Product Owner Responsibilities

Where applicable: Support the clinical drug lifecycle process as an ExpMED Product Owner. Provide leadership at product, substance and asset level.

Success Measures

Product quality, leadership effectiveness, timeline adherence, stakeholder satisfaction.

Collaboration & Digital Innovation

Promote cross-functional teamwork within ExpMED and across BI. Support innovative digital solutions. Drive predictive models and intelligent optimization approaches. Contribute to organization-wide innovation initiatives.

Success Measures

Quality of collaboration, frequency of innovative digital initiatives, stakeholder feedback.

Regulatory & Organizational Requirements

Must understand and implement: International Good Clinical Practice (GCP), Good Statistical Practice, ICH guidelines and regulations across all regions, Statistical methodology guidance, Clinical development standards, Therapeutic Area-specific requirements, BI processes, Standard Operating Procedures (SOPs), Clinical Development Plan requirements, Good Laboratory Practice (GLP), Good Manufacturing Practice (GMP).

Job Complexity

Solves complex, defined problems. Has strategic impact across the clinical drug lifecycle. Considers the needs and requirements of multiple departments and stakeholders. Influences decision-making at a broader organizational level.

Interfaces

Collaborates with: GCO, GPV, Therapeutic Areas, TMCP, GRA, Research, Development, Pharma Supply. Represents BI regarding: Clinical planning, Data transformation, Statistical analyses, Critical regulatory requests, Project and asset-level data science activities.

Experience & Expertise

Required: Strong understanding and application of data science principles. Broad expertise in planning analyses, data transformation, statistical analysis, interpretation of results, reporting. Broad knowledge and advanced experience in relevant programming/software languages. Advanced understanding of the clinical drug development lifecycle. Strong understanding of clinical trial development. Advanced project leadership experience required. PhD: 3+ years in pharmaceutical industry, CROs, regulatory authorities, or academia. MSc: 6+ years in pharmaceutical industry, CROs, regulatory authorities, or academia. Bachelor's Degree: 7+ years of data science experience. Deep subject matter expertise may partially compensate for experience requirements.

Job Impact

Responsible for: Analysis of clinical drug lifecycle data, delivering scientific insights to internal and external stakeholders, independent decision-making related to data science activities, translating data into business and scientific value. Results must be tailored to customer and stakeholder needs.

Education Requirements

Bachelor's, Master's or Doctoral degree in: Statistics, Mathematics, Computer Science, Data Science, Psychology, Finance, Related quantitative disciplines.

Required Capabilities

Statistical & Scientific Expertise: Thorough knowledge of statistical methodology, strong understanding of experimental design and clinical trials, understanding of terminology related to supported disease areas and assets, experience processing clinical trial information. Advanced Analytics: In-depth understanding of advanced statistical concepts used in Data Science. Technical Skills: Advanced working knowledge of multiple relevant software/programming languages. Leadership & Training: Ability to lead and facilitate meetings, ability to develop and deliver data science training, strong project leadership capability. Communication: Fluent English (Read / Write / Speak), strong communication and presentation skills. Collaboration: Proven ability to work within global and remote teams, strong stakeholder management skills, effective collaboration with CROs, experts and management. Problem Solving: Proactively identify issues, develop solutions, interact independently with internal and external stakeholders on data science matters. Cultural Awareness: Effective communication across local and global cultures, sensitivity to internal and external stakeholder needs.

Quick Candidate Snapshot

PhD + 3 years, MSc + 6 years, or Bachelor's + 7 years of relevant Data Science experience. Strong statistical analysis and programming expertise. Experience with clinical trial, registry and/or real-world data. Strong understanding of pharmaceutical R&D and clinical development. Advanced project leadership experience. Experience driving innovation and digital transformation. Strong stakeholder management and data storytelling capabilities. Fluent English communication skills.