Principal Data Scientist/Non-Line Manager, Experimental Medicine Japan D&A (Shinagawa, Japan, Tokyo)
About this opportunity
Basic Purpose of the Job
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
Prescriptive analytics
Works with data from:
Clinical trials
Clinical registries
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
Data Science Leadership
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
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
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:
Regulatory Requirements
International Good Clinical Practice (GCP)
Good Statistical Practice
ICH guidelines and regulations across all regions
Clinical Development Requirements
Statistical methodology guidance
Clinical development standards
Therapeutic Area-specific requirements
Internal Requirements
BI processes
Standard Operating Procedures (SOPs)
Clinical Development Plan requirements
Additional Requirements (where applicable)
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:
Data Science Expertise
Strong understanding and application of data science principles
Broad expertise in: Planning analyses
Data transformation
Statistical analysis
Interpretation of results
Reporting
Technical Expertise
Broad knowledge and advanced experience in relevant programming/software languages
Clinical Development Expertise
Advanced understanding of the clinical drug development lifecycle
Strong understanding of clinical trial development
Leadership
Advanced project leadership experience required
Experience Requirements
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
]]>
Job details
How this role compares
Computed from every other active Biostatistics & Data Science role in our database, not just this employer's listings.
We currently track 36 comparable Manager Biostatistics & Data Science roles across 15 biopharma companies.
Salary context
12 of 36 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 36 comparable roles
+ 1 more countries
Seniority mix
36 of 36 peers have a known seniority level
Therapeutic area mix
8 of 36 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
Open in 2 locations
Open in 3 locations
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.