This role supports technical and sustaining engineering activities for planning and production process improvement projects across Treat Operations, with a growing focus on data-driven decision making and business analytics. The successful candidate will develop expertise as a hybrid Process Engineer / Data Scientist, applying analytics to transform data into actionable insights, clarify operational issues in Yokohama Treat, and contribute to end-to-end solutions.
The role assists in analyzing current practices and implementing modifications to improve productivity, lead time and inventory control, and quality through data-driven and software-enabled initiatives. It conducts statistical analyses such as Design of Experiments (DOE) and Statistical Process Control (SPC) to identify physical causes of variation and support continuous improvement. The position works with product/process design, software development, validation, and site functions to ensure that processes and designs are compatible and scalable.
- Provide technical support to internal clients (Production, Planning, Training, NPD, Clinical, etc.) as part of a Global Engineering Team.
- Partner with planning, software/validation, and business analytics teams to analyze, design, and implement improvements for current processes and digital tools.
- Deploy, maintain, and improve Standard Work; support stability and scalability of core planning and production processes.
- Conduct studies to analyze root causes (Pareto, 5-Why, fishbone) and deliver feasible corrective and preventive actions.
- Execute assigned project tasks aligned with organizational goals; track benefits (productivity, lead time and inventory, cost, quality).
- Estimate manufacturing cost, determine standard times, and recommend tooling/process/automation requirements for new or existing product lines.
- Build dashboards and decision-support tools for capacity, inventory, and lead time management; maintain data sources and inputs with stakeholders.
- Perform business process and data analysis; detect gaps and opportunities; convert insights into operational actions.
- Apply the DIKW model to transform raw data into actionable insights; clarify operational problems and contribute to solutions for Treat Operations.
- Conduct hypothesis-driven analyses using statistical modeling and machine learning to support strategy validation and operational optimization (e.g., forecasting, anomaly detection, optimization).
- Develop and maintain decision support tools; present findings to support decision-making for cross-functional stakeholders.
SCOPE & COMPLEXITY
- Works on problems of moderate scope where analysis of situations or data requires a review of a variety of factors. Exercises judgment within defined procedures and practices to determine appropriate action. Expanding knowledge of related disciplinary areas. Builds productive internal and external working relationships.
EXPERTISE
- Developing professional expertise in process engineering, operations planning, and data science. Applies professional concepts and company policies and procedures to resolve a variety of issues. This is an intermediate-level position.
EDUCATION & EXPERIENCE
- Typical background of successful candidates includes:
- Educational background in engineering or a related field
- Minimum of 2 years of relevant hands-on experience in process/industrial engineering, operations planning, business analytics, data science, or manufacturing excellence
LANGUAGES
- English: Fluent (must) for global coordination
- Japanese: Business level (preferred)
- Standard Work, e.g., Lean, Six Sigma; root cause analysis; DOE/SPC; capability analysis.
- Advanced Excel, Power BI, and SQL for data extraction, transformation, and visualization; KPI design and dashboarding.
- Python or R for analytics and data science (e.g., pandas, NumPy, scikit‑learn/statsmodels, time‑series forecasting, optimization/OR).
- Experience with Power Automate for workflow automation and process optimization; familiarity with AI Agent development (e.g., Microsoft Copilot Studio) to build intelligent assistants that support operational tasks and decision-making.
- Experience building decision‑support tools for capacity, inventory, and lead time management; data quality and governance awareness.
- Basic knowledge of software development concepts and databases; familiarity with PHP, Java, or .NET is a plus.
- Basic project management (charter, schedule, risk/RAID, stakeholder management).