Senior Data Scientist

As a Senior Data Scientist, you will leverage advanced analytics, machine learning, and statistical modeling to solve complex business challenges through data-driven approaches. This role requires deep technical expertise to transform data insights into actionable business strategies, as well as strong communication skills to collaborate effectively with cross-functional teams. We welcome candidates with extensive experience in analytics, the IT/technology industry, and cutting-edge AI technologies.

What We Expect from You

As a Senior Data Scientist, you will be expected to:

• Lead the entire machine learning lifecycle for core algorithms such as recommendation engines and advertising optimization—from data exploration and feature engineering to model implementation, deployment, and production maintenance.
• Continuously improve model accuracy and business value, driving measurable revenue growth and business impact through data-driven decision making.
• Partner with business and executive stakeholders to translate business challenges into data-driven solutions, leading KPI definition and strategic initiatives.
• Mentor junior team members in both technical and non-technical areas while establishing engineering best practices, documentation standards, and a strong development culture that maximizes overall team performance.

Responsibilities

• Machine Learning for Ad Delivery Optimization. Design, develop, and maintain machine learning models that optimize personalized advertising delivery to maximize business outcomes. This includes customer lifetime value (CLV) prediction, multi-channel customer modeling, customer segmentation, and extracting actionable insights from campaign performance.
• Personalization & Recommendation Systems. Design and implement recommendation engines using rule-based methods, machine learning, and deep learning techniques to improve user engagement and strengthen brand trust.
• Advanced Customer Segmentation. Lead customer behavior analysis using clustering and other advanced segmentation techniques to develop highly personalized strategies that enhance customer engagement and brand value.
• Data Engineering & Exploratory Data Analysis (EDA). Clean, preprocess, and validate large-scale structured and unstructured datasets. Perform exploratory data analysis (EDA) to identify meaningful patterns, ensure data quality, and establish analytical strategies for solving business problems.
• KPI Design & Stakeholder Collaboration. Partner with internal teams—including Sales and Customer Success—as well as client marketing teams to define, propose, and communicate business KPIs that are both ambitious and easily understood by non-technical stakeholders.
• Continuous Model Improvement. Continuously improve model performance by conducting systematic validation, error analysis, and iterative enhancements to model architectures and feature engineering in order to achieve business KPIs.
• Collaborative Development & Responsible AI. Manage production-grade code repositories using GitHub while maintaining best practices in version control and documentation throughout the research and development lifecycle. Ensure compliance with data privacy requirements and responsible AI principles.
• Research & Application of Cutting-Edge AI Technologies. Research and implement state-of-the-art AI technologies, including deep learning, natural language processing (NLP), large language models (LLMs), and Generative AI, to solve business challenges and enhance product and brand value.

Requirements

• 6+ years of professional experience in Data Science, with strong expertise across the following areas:
  • End-to-End Machine Learning Lifecycle
    • Proven experience leading the complete machine learning lifecycle, including data exploration, feature engineering, KPI definition, model development and validation, system implementation, performance evaluation, and deployment, operation, and maintenance of ML systems in production environments.
  • Programming & Code Quality
    • Strong programming skills in Python and SQL.
    • Ability to write clean, readable, maintainable, and production-quality code while following software engineering best practices.
  • Machine Learning & Statistics
    • Strong understanding of machine learning fundamentals for structured and time-series data, including prediction, classification, regression, clustering, and statistical modeling.
    • Hands-on experience applying libraries such as NumPy, Scikit-learn, and PyTorch to solve real-world business problems.
  • Data & Analytics Technologies
    • Practical experience with SQL databases (e.g., MySQL, PostgreSQL), data warehouses, and OLAP platforms such as Snowflake and Amazon Redshift.
  • Model Evaluation & Experimentation
    • Experience conducting iterative hypothesis-driven analysis and offline model evaluation using techniques such as cross-validation.
    • Practical knowledge of A/B testing and fundamental statistical analysis methodologies.
  • Cloud Platforms
    • Hands-on experience with major cloud platforms, including AWS, Azure, or Google Cloud Platform (GCP), with AWS experience preferred.
  • Development Environment & Collaboration
    • Experience working in Linux environments and using development tools such as VS Code, Git/GitHub, Docker, and CI/CD pipelines (e.g., GitHub Actions).
    • Proven experience collaborating with engineering teams to deploy and integrate machine learning models into production systems.
• Language Requirements
  • Japanese: Business-level proficiency or higher, including the ability to communicate effectively in spoken and written Japanese and to read technical and business documentation.
  • English: Business-level proficiency or higher, as English is the primary language used for communication within the team.

Nice to haves

While not specifically required, tell us if you have any of the following.

• Recommendation Systems Expertise
  • Hands-on experience designing and developing recommendation systems using techniques such as Collaborative Filtering, Content-Based Filtering, Matrix Factorization, Neural Collaborative Filtering (NCF), Two-Tower architectures, and other modern recommendation algorithms.
• Advanced Machine Learning Engineering
  • Experience with online inference (online serving), model deployment, and production-grade ML workflows, including feature pipelines, model monitoring, retraining, continuous improvement cycles, and MLOps best practices.
  • Experience designing and developing microservice architectures using frameworks such as FastAPI or Flask.
  • Experience fine-tuning open-source deep learning models, including embedding models, sequence models, multi-task learning architectures, and other state-of-the-art AI models.
  • Experience developing and operating high-QPS (Queries Per Second), low-latency machine learning APIs in production environments.
• Data Engineering & Orchestration
  • Experience designing and operating scalable data pipelines using workflow orchestration tools such as Apache Airflow or AWS Step Functions.
  • Hands-on experience with streaming and messaging technologies such as AWS Kinesis and Apache Kafka.
  • Experience working with distributed computing and large-scale data processing frameworks such as Apache Hadoop, Apache Spark, or MPI.
• Domain Expertise & Business Acumen
  • Experience working on projects related to ranking systems, search, advertising optimization, personalization, or similar data-driven products.
  • Ability to translate business objectives and domain knowledge into actionable data analysis and data-driven improvement initiatives.
  • Strong understanding of user behavior analytics, business growth metrics, and optimization from a business impact perspective.
• Community Involvement & Research
  • Demonstrated passion for applied machine learning through contributions to open-source software (OSS), published research papers, participation in Kaggle or similar competitions, or other meaningful contributions to the machine learning community.

Compensation

¥8,000,000 ~ ¥11,000,000 annually.