Lead Data Scientist
Location: Japan
Language Requirements: Japanese (Advanced), English (Intermediate)
Position Overview
The Lead Data Scientist drives advanced analytics initiatives to enhance customer engagement and deliver actionable business insights. This role partners closely with business stakeholders—primarily in Japanese—to understand requirements and lead data projects from planning through implementation and delivery.
Key Responsibilities
Business Collaboration & Requirements
Work with business stakeholders to gather requirements and understand broader business needs.
Translate business challenges into data-driven analytical solutions.
Analytics & Project Leadership
Lead end-to-end analytics initiatives, including planning, execution, and delivery.
Oversee model development, deployment, and lifecycle management to ensure alignment with business objectives.
Produce clear technical documentation, project deliverables, and reports.
Cross-Functional Coordination
Collaborate with data platform teams and IT teams to identify dependencies and remove blockers.
Ensure smooth development workflows using CI/CD, MLOps, and cloud technologies.
Technical Execution
Build and implement models using machine learning methods (classification, regression, clustering, time-series analysis, dimensionality reduction).
Develop analytics solutions using SQL, Python, Pandas, PySpark, AzureML, and similar tools.
Create dashboards and visualizations with PowerBI.
Maintain responsible and compliant AI practices.
Domain Expertise
Apply knowledge of financial or insurance sectors—especially in distribution, operations, or marketing—to improve analytical outcomes.
Incorporate best practices and methodologies from other regions to enhance analytics performance.
Innovation & Continuous Improvement
Propose new analytical solutions based on emerging technologies such as Generative AI.
Continuously upgrade domain knowledge and assess its business relevance.
Communication
Present insights, progress updates, and results to team members and leadership in a clear and concise manner.
Explain complex analytical concepts to diverse non-technical audiences.
Requirements
Experience
5+ years in advanced analytics or data science roles with demonstrable business impact.
Leadership experience managing data science or analytics teams.
Technical Skills
Strong quantitative and analytical problem-solving skills.
Hands-on experience with machine learning techniques and model deployment.
Proficiency in Python and SQL.
Deep understanding of data visualization, particularly PowerBI.
Experience with CI/CD, MLOps, and cloud platforms such as Azure.
Practical knowledge of Pandas, PySpark, AzureML, and related tools.
Background in financial or insurance industries preferred.
Leadership Competencies
Ability to build trust and influence colleagues across all levels.
Strong interpersonal skills and cross-functional collaboration abilities.
Excellent communication skills for delivering complex information clearly.
Education
Bachelor’s degree in a quantitative field (Statistics, Marketing Science, Operations Research, Econometrics, Machine Learning, etc.).
Advanced degree is a plus.
Relevant certifications (e.g., cloud data certifications) preferred.
Additional Information
The ideal candidate is a proactive leader with strong technical expertise and the ability to drive data initiatives in complex business environments. A background in financial or insurance sectors and familiarity with modern analytics tools is highly valued.
Benefits
Flexible working arrangements and hybrid work options.
Comprehensive time-off programs, including national holidays and special leave.
Full social insurance and transportation reimbursement.
Employee wellness support, training opportunities, and language learning discounts.
Support for work–life balance, including flexible schedules and family-friendly policies.
Casual dress code and additional lifestyle benefits depending on internal policy.