Tech Bilingual Data Scientist 【Global AI Company】Machine Learning Engineer | Up to ¥14M | Tokyo Hybrid

Position Overview

Salary: ¥8,000,000 – ¥14,000,000 annually

Location: Tokyo (Aoyama)

Work Style: Hybrid – approximately 4 days remote per week (1 office day recommended)

Working Hours: Flextime (Core Hours: 10:00–16:00)

Employment: Permanent / Full-Time

Japanese: Business Level Required

English: Ability and willingness to communicate in technical discussions

About the Company

One of our key clients, a fast-growing Japanese AI and MarTech SaaS company, is looking for an experienced Senior Data Scientist / Machine Learning Engineer to join its global Data & AI team.

The company develops AI-powered customer engagement and marketing automation platforms that help enterprise businesses optimize customer communication, improve engagement, and drive revenue growth.

With approximately ¥6.3 billion in total funding, rapid business expansion, and an international workforce representing around 20 nationalities, the company offers an exciting opportunity to work on large-scale AI products in a global environment.

The organization develops its own AI and data capabilities, leveraging proprietary first-party data and customer-provided zero-party data to build advanced machine learning solutions.

Role Overview

As a Senior Data Scientist / Machine Learning Engineer, you will develop and deploy advanced machine learning models that directly impact business growth and customer engagement.

You will work on recommendation systems, personalized marketing, customer segmentation, predictive analytics, and advertising optimization.

This is a hands-on position covering the complete machine learning lifecycle, from exploratory data analysis and feature engineering to model deployment, monitoring, and continuous optimization.

You will collaborate with international engineers, data scientists, product teams, and business stakeholders to transform complex business challenges into scalable AI solutions.

Key Responsibilities

• Develop and deploy machine learning models for personalized marketing and advertising optimization.
• Design and implement recommendation engines using machine learning and deep learning techniques.
• Build predictive models for customer lifetime value (CLV), customer behavior, and engagement.
• Develop advanced customer segmentation models using clustering and behavioral analytics.
• Perform exploratory data analysis (EDA), data preprocessing, and feature engineering.
• Lead end-to-end machine learning development, including training, validation, deployment, and monitoring.
• Design and improve ML pipelines and production-level machine learning systems.
• Define business KPIs and translate analytical insights into actionable recommendations.
• Conduct A/B testing, statistical analysis, and model performance evaluations.
• Collaborate with engineering teams to integrate ML models into production environments.
• Research and evaluate emerging technologies, including Generative AI, LLMs, NLP, and deep learning.
• Mentor junior team members and contribute to engineering standards and documentation.

Requirements

• 4+ years of professional experience in Data Science.
• Strong knowledge of machine learning, statistical modeling, and predictive analytics.
• Professional experience using Python and SQL.
• Experience with machine learning libraries such as Scikit-learn, NumPy, or PyTorch.
• Experience working with structured data, including tabular and time-series datasets.
• Hands-on experience with classification, regression, clustering, and predictive modeling.
• Experience across the complete machine learning lifecycle, including model development, deployment, and production maintenance.
• Knowledge of feature engineering, model evaluation, cross-validation, and A/B testing.
• Familiarity with Linux, Git/GitHub, Docker, and CI/CD environments.
• Experience collaborating with software engineers to integrate ML models into production systems.
• Business-level Japanese communication skills.
• Willingness to communicate and collaborate in English within an international engineering environment.

Preferred Qualifications

• Experience building recommendation systems, ranking algorithms, or personalization engines.
• Knowledge of collaborative filtering, matrix factorization, neural collaborative filtering, or Two-Tower models.
• Experience with MLOps, model monitoring, retraining pipelines, and online inference.
• Experience with AWS, GCP, or Azure, particularly AWS.
• Experience with Snowflake, Redshift, PostgreSQL, or other data platforms.
• Knowledge of Apache Airflow, AWS Step Functions, Apache Spark, or Kafka.
• Experience developing scalable ML APIs using FastAPI or Flask.
• Experience with high-throughput, low-latency machine learning services.
• Experience with deep learning, NLP, LLMs, or Generative AI.
• Experience in advertising technology, recommendation systems, search, ranking, or marketing analytics.
• Contributions to open-source projects, research publications, or Kaggle competitions.

Ideal Candidate

We are looking for a hands-on Data Scientist or Machine Learning Engineer who enjoys solving real business problems through machine learning.

The ideal candidate combines strong analytical capabilities with practical software engineering skills and experience deploying ML models into production.

You should be comfortable working with large datasets, collaborating with international teams, and communicating technical insights to business stakeholders.

Experience in recommendation systems, personalization, advertising optimization, or customer behavior modeling would be particularly valuable.

Benefits

• Hybrid working environment with flexible working arrangements.
• International engineering team with members from approximately 20 nationalities.
• Flextime system with core hours from 10:00 to 16:00.
• Opportunities to develop production-level AI and machine learning systems.
• Access to large-scale proprietary datasets.
• Professional development support, including up to ¥55,000 annually for books and seminars.
• Social insurance and transportation allowance.
• Paid annual leave, summer holidays, and year-end holidays.
• Health and wellness support programs.
• Opportunities for career advancement into ML Tech Lead, Principal Data Scientist, or AI/ML Engineering Management roles.