Bilingual Data Scientist, Machine Learning Engineer

GAIDOOR RECRUIT

. Develop and deploy machine learning models for personalized marketing and advertising optimization.

Posted 10/24/2026full-timeTokyo • JapanMid-LevelSenior💰 ¥8,000,000 - ¥14,000,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine learning models, with a strong focus on personalized marketing and advertising optimization. Proficient in statistical modeling, predictive analytics, and collaboration within international engineering teams.

Highest-signal resume keywords

Machine Learning DevelopmentPython ProgrammingSQL ProficiencyPredictive ModelingA/B Testing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingPredictive AnalyticsFeature EngineeringClusteringRegressionClassificationData PreprocessingExploratory Data AnalysisModel Evaluation

Soft Skills

CollaborationMentoringCommunication

Tools & Technologies

Scikit-learnNumPyPyTorchDockerGitAWSGCPAzureFastAPIFlask

Industry Keywords

Personalization EnginesRecommendation SystemsAdvertising TechnologyMarketing AnalyticsGenerative AINLPDeep LearningMLOpsData PlatformsHigh-Throughput Services

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSAzureDockerFlaskGoogle Cloud PlatformKafkaLinuxNumpyPostgresPythonPyTorchScikit-LearnSparkSQL

About the role

Key responsibilities & impact

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, customer behavior, and engagement.
Develop advanced customer segmentation models using clustering and behavioral analytics.
Perform exploratory data analysis, 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.
Collaborate with international engineers, data scientists, product teams, and business stakeholders to create scalable AI solutions.

Requirements

What you’ll need

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

Benefits

Comp & perks

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.