Job Title
Analytics Engineer (Data Scientist)
Location
Tokyo, Japan
Workplace Type
Hybrid
Company Overview
An innovative data and AI technology company is seeking an Analytics Engineer (Data Scientist) to support the development of advanced analytics solutions and data-driven products. The organization leverages alternative data, large-scale datasets, machine learning, and generative AI technologies to help enterprises, financial institutions, and public sector organizations make better business decisions.
Role Overview
This position combines Data Science, Analytics Engineering, and Product Development responsibilities. The successful candidate will work with large-scale datasets to generate business insights, develop predictive models, build analytical infrastructure, and contribute to the creation of data products used across multiple industries.
Key Responsibilities
Analyze large-scale structured, unstructured, and alternative datasets to uncover business insights.
Develop predictive models, forecasting solutions, and advanced analytical frameworks.
Support data-driven decision-making for clients across multiple industries, including finance, real estate, retail, and public sector organizations.
Design, build, and maintain analytical data models, data marts, and reporting environments.
Develop, validate, and deploy machine learning models for production use.
Collaborate with product teams, software engineers, and business stakeholders to deliver scalable data solutions.
Transform complex business requirements into actionable analytical solutions.
Create dashboards, reports, and visualizations to communicate insights effectively.
Support continuous improvement of data pipelines, analytics workflows, and machine learning operations.
Contribute to the development of innovative data products leveraging emerging AI technologies.
Required Qualifications
Experience using Python for data analysis, statistical modeling, and data processing.
Hands-on experience developing and implementing machine learning models.
Experience gathering business requirements and translating them into analytical solutions.
Ability to communicate technical findings and business insights to stakeholders.
Business-level Japanese communication skills.
Ability to read and interpret Japanese business documentation and financial information.
Strong analytical thinking and problem-solving capabilities.
Preferred Qualifications
Experience with data visualization and business intelligence tools such as:
Tableau
Looker
Power BI
Experience designing and operating MLOps environments.
Backend software development experience.
Experience working with cloud-based data platforms and large-scale datasets.
Knowledge of modern data architecture and analytics engineering best practices.
Experience supporting production machine learning systems.
Technical Environment
Python
Machine Learning Frameworks
Data Analytics Platforms
Business Intelligence Tools
Data Warehousing Solutions
Cloud Infrastructure
Data Pipelines & ETL Processes
MLOps & Model Deployment Frameworks
Generative AI Technologies
Large-Scale Data Processing Platforms
Core Competencies
Data Science
Analytics Engineering
Machine Learning
Forecasting & Predictive Modeling
Business Analytics
Data Pipeline Development
Product Analytics
Data Visualization
Stakeholder Management
Problem Solving
Ideal Candidate Profile
Passionate about transforming data into impactful business solutions.
Interested in building scalable products rather than producing one-time analyses.
Comfortable working at the intersection of data science, engineering, and business strategy.
Strong communicator capable of presenting insights to both technical and non-technical stakeholders.
Curious about emerging technologies, including AI and advanced analytics.
Self-motivated with a proactive approach to learning and innovation.
Enjoys collaborating within cross-functional product and engineering teams.
What This Role Offers
Opportunity to work with unique alternative and large-scale datasets.
Exposure to cutting-edge machine learning and generative AI technologies.
Ability to build data products that directly influence business decisions.
Collaborative environment combining data science, engineering, and product development.
Flexible hybrid working model.
Strong opportunities for professional growth within a data-driven technology organization.