Senior Data Scientist

Required Qualifications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.