Operations Research Analyst

Formosa Plastics•Livingston, NJ

1d•Onsite

About The Position

An Operations Research Analyst wanted by Formosa Plastics Corporation, U.S.A. in Livingston, New Jersey. This role involves applying mathematical modeling and statistical analysis to optimize petrochemical manufacturing performance, analyzing datasets from ERP systems to enhance reliability and cost performance, and establishing predictive models for mechanical assets. The analyst will also develop asset damage mechanisms and corrosion analysis, create predictive and forecasting models for various operational aspects, and conduct optimization modeling for process modifications and investments. Additionally, the role includes designing KPI statistical models, optimizing SOPs, conducting enterprise-level decision-making, and collaborating with operations and engineering teams.

Requirements

Master’s degree in Operations Research or Data Science
At least One (1) year of experience in quantitative data analysis and supply chain
Knowledge of ERP, supply chain KPI, Python, data management, statistical inference, R, big data, SQL, Spark, Hadoop MapReduce, relational database, sampling techniques, Random Forest Models, Clustering, Anomaly Detection, K-means, machine learning, Logistic Regression, Neural Networks, Time Series, nonlinear optimization, Stochastic Programming, data mining, visualization, Power BI, Tableau

Responsibilities

Apply mathematical modeling and statistical analysis to optimize petrochemical manufacturing performance under engineering and operational constraints.
Analyze production, maintenance, and inventory system-level datasets from ERP systems such as AS400 to enhance operational reliability and cost performance.
Establish predictive and reliable statistical modeling of rotating and stationary mechanical assets by evaluating maintenance records and operating conditions to enhance equipment lifecycle performance.
Develop asset damage mechanisms and corrosion analysis to support risk-based inspection and mechanical integrity management.
Develop predictive and forecasting models to support production scheduling, maintenance optimization, and supply chain coordination under volatile operational conditions.
Conduct optimization modeling of process modifications, maintenance strategies, and capital investments using cost–benefit analysis to support data-driven decision-making.
Design enterprise-level KPI statistical models to optimize production efficiency and operational risk across integrated manufacturing systems.
Optimize enterprise Standard Operating Procedures (SOPs) by applying statistical process analysis and predictive modeling to improve mechanical integrity, regulatory compliance, and operational consistency.
Conduct enterprise-level decision-making by validating predictive and optimization models, ensuring data integrity across maintenance and operations systems, and maintaining regulatory and business alignment.
Collaborate with operations and engineering teams to integrate mechanical and process systems into quantitative frameworks for operational improvement.