About the Opportunity
We are currently partnering with one of Japan’s fast-growing AI & DX companies, which is building a new Data Science organization focused on developing an advanced AI-ready data and machine learning platform.
The company is looking for a highly experienced Senior Data Scientist / ML Platform Lead to play a key role in the design, development, and operation of its “Skill Data Cloud” and skill ontology infrastructure.
JAPANESE BUSINESS LEVEL REQUIRED
This is not a traditional Data Scientist position focused only on model development or analysis.
You will work across Data Science, Machine Learning Engineering, MLOps, Data Platform Architecture, and technical project leadership, helping establish the technical foundation and operating standards of a newly forming Data Science team.
You will also work closely with business leaders and other stakeholders to translate business requirements into scalable data and AI solutions.
Position
Senior Data Scientist / ML Platform Lead
Solution Development & New Team Build-up
Salary
¥12,000,000 – ¥18,000,000 JPY annually
Compensation will be determined based on current salary, experience, and technical capabilities.
The company conducts performance, salary, and promotion reviews twice per year.
An incentive program is also available, with incentives paid twice per year depending on individual performance.
Mission
Your primary mission will be to lead the design, development, and operation of the Skill Data Cloud and its skill ontology layer, transforming them into a scalable and AI-friendly Data / ML platform.
You will help establish the architecture and technical standards required to integrate diverse data sources, including project experience, learning history, assessments, and other skill-related information.
In addition to hands-on technical responsibilities, you will provide technical direction to Data Scientists, lead projects, and collaborate closely with the business organization.
Responsibilities
Data & Skill Ontology Architecture
- Design and maintain skill taxonomies and ontology structures
- Model relationships between skills and establish processes for managing taxonomy changes
- Define organization-wide data quality standards, governance rules, and metrics
- Develop technical roadmaps for the evolution of the Skill Data Cloud
Data Platform Development
- Lead the architecture, development, and operation of the Skill Data Cloud
- Integrate multiple internal and external data sources
- Design scalable, AI-friendly data infrastructure
- Establish architectural guidelines for future Data Science and AI applications
- Build and improve data pipelines supporting ML and Generative AI use cases
Machine Learning / Generative AI
- Lead the design, implementation, and evaluation of ML and Generative AI models
- Define model architecture strategies and technical standards
- Develop models for analyzing relationships between skills and other business data
- Establish model quality, risk management, and evaluation frameworks
- Translate experimental models and PoCs into production-ready solutions
MLOps & Production ML
- Design and develop scalable MLOps infrastructure
- Establish ML pipelines covering training, evaluation, deployment, and monitoring
- Introduce and operate technologies such as MLflow, Feature Stores, and Model Registries
- Design model inference infrastructure running on Kubernetes
- Establish CI/CD standards for model deployment
- Design and operate model APIs and containerized ML services
- Define model monitoring, A/B testing, and quality-management standards
Technical & Project Leadership
- Develop project plans and technical roadmaps
- Provide technical direction to Data Scientists
- Coordinate requirements and priorities with business stakeholders
- Lead Data Science / AI projects from requirements definition through production implementation
- Establish technical and operational standards for the newly forming Data Science organization
- Contribute to team development, organizational design, and future hiring strategy
Why Join?
Direct Impact on Business & Data Strategy
You will work directly with senior management and business leaders to develop medium- and long-term Data / AI strategies.
Rather than simply implementing predefined requirements, you will help determine how technology and Data Science should support the company’s overall business strategy.
Build a Large-Scale ML & Data Platform from the Ground Up
You will have the opportunity to lead the development of the Skill Data Cloud, including its AI-ready data infrastructure and large-scale MLOps environment.
The scope includes Data Platform Architecture, Machine Learning infrastructure, external data integration, and potentially broader ecosystem and partnership strategies.
Build a New Data Science Organization
This is also a team-building opportunity.
You will help establish the technical and operational standards of the Skill Intelligence Lab and provide direction to Data Scientists.
As the organization grows, you may also contribute to organizational design, hiring strategy, and the future development of the team.
International Environment
The company has employees from more than 20 different countries, with approximately 22% of employees being non-Japanese nationals.
You will work in an environment that combines Japanese business operations with an increasingly international engineering organization.
Technology Environment
Programming:
- Python
Cloud / Infrastructure:
- Public Cloud platforms
- Kubernetes
- Containerized environments
MLOps:
- MLflow
- Model Registry
- Feature Store
- Experiment Management Tools
Data:
- Data Warehouse (DWH)
- Data Pipelines
- Data Platform Architecture
DevOps:
- CI/CD
- IAM
Hardware:
- Windows or MacBook
- Latest-generation MacBook Pro devices are commonly provided
Requirements
Must Have
- 5+ years of professional experience developing data-driven solutions using Python
Preferred Experience
We are particularly interested in candidates with experience in several of the following areas:
- Professional experience as a Data Scientist, ML Engineer, AI Engineer, or similar role
- Design, development, and operation of production ML pipelines
- Python-based Machine Learning development
- Cloud-based ML / Data infrastructure
- Kubernetes and containerized environments
- MLOps architecture and implementation
- MLflow or similar experiment-management platforms
- Feature Store and Model Registry implementation
- Data extraction and preprocessing from existing DWH / Data Platforms
- Feature engineering
- CI/CD pipelines for ML models and APIs
- Building and operating container images
- Model deployment and production inference infrastructure
- Model monitoring and quality management
- A/B testing design and operation
- Experience taking notebook or PoC-level Data Science code into production
- Data Engineering and Data Pipeline development
- Working directly with business teams on Data / AI initiatives
- Requirements definition, prioritization, and project direction
- Technical leadership or collaboration with Data Science teams
Location & Working Style
Shibuya, Tokyo
Office:
Shibuya-ku, Tokyo
Hybrid / remote work
Selection Process
Document Screening
↓
1–2 Online Interviews / Meetings
- IT Literacy Assessment
↓
Final Interview
↓
Offer
The overall selection process typically takes approximately one month.