Process Engineer(Pipeline Req)

Role summary

This role is responsible for providing technical and sustaining engineering support for planning and production process improvement projects across Treat Operations, with a strong focus on data-driven decision making and business analytics. The successful candidate will act as a hybrid Process Engineer / Data Scientist, leveraging advanced analytics to transform data into actionable insights, clarify operational issues in Yokohama Treat, and deliver end-to-end solutions.

The role analyzes current practices and implements modifications to improve productivity, lead time and inventory control, and quality through data-driven and software-enabled initiatives. It develops and conducts statistical analyses such as Design of Experiments (DOE) and Statistical Process Control (SPC) to identify physical causes of variation and drive continuous improvement. The position works closely 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; drive 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.
Plan, prioritize, and execute projects 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; proactively detect gaps and opportunities; convert insights into operational actions.
Apply the DIKW model to transform raw data into actionable wisdom; clarify operational problems and deliver solutions for Treat Operations.
Lead hypothesis-driven analyses using statistical modeling and machine learning to validate strategies and optimize operations (e.g., forecasting, anomaly detection, optimization).
Develop and maintain decision support tools; present key findings to influence decision-making for cross-functional stakeholders.
Influence and negotiate with global stakeholders to align requirements, timelines, and trade-offs; manage risks and facilitate change adoption.

SCOPE & COMPLEXITY

Works on problems of diverse scope where analysis of data requires evaluation of identifiable and ambiguous factors. Demonstrates sound judgment in selecting methods and techniques for obtaining solutions. Resolves a wide range of issues in creative, pragmatic ways, and networks with senior internal and external personnel in area(s) of expertise.

EXPERTISE

A seasoned, experienced professional with a full understanding of process engineering, operations planning, and data science. Fully qualified, career‑oriented, and expected to independently contribute while influencing others through expertise and data.

SUPERVISION

Structures and manages the execution of project/program tasks with general supervision and limited guidance; recommends new approaches; mentors junior engineers/analysts as needed.

EDUCATION & EXPERIENCE

Bachelor’s degree in Engineering, Industrial Engineering, Operations Research, Computer Science, or Data Science required; Master’s preferred.
Typically 4–6 years of directly related experience in process/industrial engineering, operations planning, business analytics, data science, or manufacturing excellence.

LANGUAGES

English: Fluent (must) for global negotiation/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 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).