Bilingual Senior Data Scientist / ML Engineer

Bilingual Senior Data Scientist / ML Engineer

Our client is looking for a Senior Data Scientist / Machine Learning Engineer to join its international Data & AI team.

You will apply machine learning, statistical modeling and advanced analytics to solve complex product and business challenges. The role covers the full ML lifecycle, from exploratory analysis and KPI definition to model development, production deployment, monitoring and continuous improvement.

This is not a research-only position. You will translate business needs into practical ML initiatives and demonstrate measurable impact on revenue, customer engagement, operational efficiency and other business KPIs.

Key Responsibilities

Machine Learning and Delivery Optimization

• Develop and operate ML models that optimize the content, timing and targeting of customer communications.
• Build models for customer lifetime value prediction, customer behavior analysis and delivery optimization.
• Analyze campaign results and translate findings into actionable recommendations.

Personalization and Recommendation Systems

• Design and implement recommendation engines using rule-based, machine learning and deep learning approaches.
• Improve engagement and retention through personalized customer experiences.
• Evaluate model performance using both offline metrics and business KPIs.

Customer Segmentation and Data Analysis

• Lead behavioral analysis using clustering and other unsupervised learning techniques.
• Develop actionable customer segments for personalized marketing strategies.
• Clean, preprocess and validate large structured and unstructured datasets.
• Conduct exploratory analysis and support scalable feature and analytical pipelines.

KPI Design and Stakeholder Engagement

• Collaborate with sales, customer success, product and client-side marketing teams.
• Translate business requirements into data science and ML initiatives.
• Define clear and measurable KPIs for technical and non-technical stakeholders.
• Estimate and communicate business impact, including ROI improvement and revenue uplift.

Production ML and MLOps

• Lead the end-to-end ML lifecycle, including feature engineering, training, validation, deployment and maintenance.
• Identify performance issues through structured validation and error analysis.
• Continuously improve models, features and evaluation methods.
• Support online inference, model retraining, monitoring and MLOps processes.

Engineering Quality and Technical Leadership

• Maintain production-quality code using GitHub or similar platforms.
• Apply appropriate documentation, testing and version-control practices.
• Collaborate with engineers to integrate models into production systems.
• Ensure data privacy and responsible AI practices.
• Mentor junior members and contribute to team standards and development processes.
• Evaluate emerging technologies in deep learning, NLP, LLMs and generative AI.

Current AI Initiatives

Key areas include:

• Recommendation and personalization
• Customer segmentation
• Message delivery optimization
• Customer lifetime value prediction
• Marketing and customer behavior analytics
• Voice and conversational AI
• Classification models for automated calling systems

The successful candidate will initially focus on recommendation, segmentation and delivery optimization while supporting other applied AI projects.

Why Join?

Unique, Large-Scale Data

Work with first-party behavioral data and customer-provided data to build highly targeted models that directly influence engagement and commercial outcomes.

End-to-End Ownership

Own the full ML lifecycle, including data exploration, model development, feature pipelines, deployment, monitoring and continuous optimization.

Direct Business Impact

Go beyond model accuracy and contribute to measurable outcomes such as engagement, conversion, retention, ROI and revenue growth.

Global AI Team

Collaborate with AI and engineering professionals across Japan and other Asian locations. English is used for technical collaboration, while Japanese is used with local business stakeholders.

Career Development

Potential career paths include:

• Data Science or ML Engineering Tech Lead
• Data Science or ML Engineering Manager
• ML Architect or Principal Data Scientist
• Data Product Manager or AI Consultant
• Global AI Project Lead

Working Environment

• Hybrid work with approximately one office day per week recommended
• Tokyo or Osaka office
• Flextime system
• Annual learning and book allowance of up to JPY 55,000
• Company laptop and transportation allowance
• Social insurance and health check support
• Childbirth and childcare support
• Additional health-related leave
• OKR-based performance reviews and ongoing feedback

Required Experience

• At least four years of professional experience in data science, machine learning or a closely related field.
• Strong knowledge of machine learning and statistics, including prediction, classification, regression and clustering.
• Experience applying machine learning to tabular or time-series data.
• Strong hands-on programming skills in Python and SQL.
• Practical experience with libraries such as NumPy, Pandas, Scikit-learn, SciPy, PyTorch, TensorFlow or Keras.
• Experience working through the complete machine learning lifecycle, including:
  • Exploratory data analysis
  • Feature engineering
  • KPI definition
  • Model training
  • Model validation
  • System implementation
  • Performance evaluation
  • Production operation and maintenance
• Knowledge of hypothesis testing, cross-validation, offline model evaluation, A/B testing and fundamental statistical analysis.
• Experience integrating machine learning models into production environments in collaboration with engineering teams.
• Experience with Linux, Git-based version control and Docker.
• Ability to write readable, maintainable and production-quality code.
• Business-level Japanese, including speaking, reading and writing.
• Ability and willingness to participate in technical communication in English.
• Strong communication skills and an interest in working with customer success, sales, product and client-facing stakeholders.

Preferred Experience

• Experience with recommendation systems, including one or more of the following:
  • Collaborative filtering
  • Content-based filtering
  • Matrix factorization
  • Neural collaborative filtering
  • Two-tower models
• Experience in ranking, search, advertising, personalization or customer engagement systems.
• Experience with model deployment, online serving, monitoring, retraining and production ML workflows.
• Experience building low-latency, high-QPS machine learning APIs.
• Experience with FastAPI, Flask or other frameworks used in microservice environments.
• Experience with AWS, Azure or GCP, preferably AWS.
• Experience with MySQL, PostgreSQL or similar SQL databases.
• Knowledge of Snowflake, Redshift or other data warehousing and OLAP solutions.
• Experience with Airflow, AWS Step Functions or other workflow-orchestration tools.
• Knowledge of Kafka, Kinesis or other streaming and messaging technologies.
• Experience with Spark, Hadoop, MPI or other distributed data-processing frameworks.
• Experience fine-tuning open-source deep learning models.
• Knowledge of embeddings, sequence models or multi-task learning.
• Experience analyzing user behavior, growth metrics and business optimization opportunities.
• Contributions to open-source projects, publications, Kaggle competitions or other applied machine learning activities.
• Mentoring or technical leadership experience.

Required Technical Stack

Programming Languages

• Python
• SQL

Core Data and ML Libraries

• Pandas
• NumPy
• Scikit-learn or SciPy

Machine Learning

Experience with one or more of the following:

• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines
• XGBoost
• LightGBM
• CatBoost
• K-Nearest Neighbors
• K-Means
• K-Modes
• DBSCAN
• Hierarchical Clustering

Deep Learning

One or more of:

• PyTorch
• TensorFlow
• Keras

MLOps and Development

• Docker
• Linux
• Git, GitHub or GitLab

Ideal Candidate

The ideal candidate can combine strong machine learning expertise with practical business judgment.

You should be comfortable working with both engineers and non-technical stakeholders, translating complex analytical results into clear recommendations and measurable business outcomes. You will need to understand business requirements, identify the right KPIs and explain the potential commercial value of a model to internal stakeholders or clients.

Because this role works closely with Japan-based customer success and sales teams, knowledge of Japanese business practices and the domestic digital communication market will be highly valuable.

Desired Skills and Experience Python

SQL

Machine Learning

Data Science

Statistical Modeling

Recommendation Systems

MLOps

PyTorch

Scikit-learn

Docker

AWS

A/B Testing

Data Analysis

Predictive Analytics

Customer Segmentation