Senior Data Scientist ML Platform Lead | 12M–18M | AI & Data Platform
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Monthly Salary:
Experience Required:
Posted:
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Job Summary
We are currently partnering with one of Japans 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.
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.
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.
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.
- 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
- 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
- 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
- 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
- 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
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 companys overall business strategy.
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.
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.
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.
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
- 5 years of professional experience developing data-driven solutions using Python
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
Relevant certifications are welcome but not mandatory.
Examples include:
- AWS Certified Machine Learning
- AWS Certified Data Analytics
- AWS Certified DevOps Engineer Professional
- AWS Certified Solutions Architect
- Google Cloud Professional Data Engineer
- TensorFlow Developer Certificate
- CKA Certified Kubernetes Administrator
- PMP
- CompTIA Security
- Relevant Data Science AI Statistics Database or IT certifications
This position would be particularly suitable for someone who:
- Has strong technical expertise but also understands business objectives
- Can act as a bridge between Data Science / Engineering teams and business stakeholders
- Enjoys building frameworks and technical standards from an ambiguous or early-stage environment
- Can involve multiple departments and drive cross-functional initiatives
- Is comfortable providing technical direction to other Data Scientists
- Enjoys building new teams processes and technical foundations
- Wants to influence both technical strategy and business strategy
Shibuya Tokyo
Access:
- Approximately 7 minutes from Shibuya Station
- Approximately 5 minutes from Shinsen Station
Hybrid / remote work is available with manager approval.
Important: This position belongs to the business-side organization and does not operate under the companys standard flextime system.
Due to client security requirements work must generally be performed from within Japan or from locations specifically approved by the company and its clients.
Company or client-provided devices may not be taken outside Japan without prior authorization.
- Full social insurance
- Kanto IT Software Health Insurance
- Transportation allowance
- Family allowance: 10000 per month for each child under 18
- Certification support
- Professional learning support
- Access to internal multi-LLM platform
- Regular health examinations
- Company-provided PC and phone
- Free drinks
- Visa and English-language support
- Club activity subsidies
- Company-wide meetings and recognition programs twice per year
- 122 annual holidays
- Five-day workweek
- Saturdays Sundays and national holidays off
- Year-end/New Year holidays
- Paid annual leave
- Special leave
- Maternity and childcare leave
Document Screening
12 Online Interviews / Meetings
- IT Literacy Assessment
Final Interview
Offer
The overall selection process typically takes approximately one month.
Required Skills:
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 Preferred Certifications Relevant certifications are welcome but not mandatory. Examples include: AWS Certified Machine Learning AWS Certified Data Analytics AWS Certified DevOps Engineer Professional AWS Certified Solutions Architect Google Cloud Professional Data Engineer TensorFlow Developer Certificate CKA Certified Kubernetes Administrator PMP CompTIA Security Relevant Data Science AI Statistics Database or IT certifications Ideal Candidate This position would be particularly suitable for someone who: Has strong technical expertise but also understands business objectives Can act as a bridge between Data Science / Engineering teams and business stakeholders Enjoys building frameworks and technical standards from an ambiguous or early-stage environment Can involve multiple departments and drive cross-functional initiatives Is comfortable providing technical direction to other Data Scientists Enjoys building new teams processes and technical foundations Wants to influence both technical strategy and business strategy
