IT Data Analyst

The Data Scientist applies statistics, machine learning, optimization, and programming to manufacturing data to improve safety, quality, throughput, cost, equipment reliability, and decision-making across plants. Develop, validate, document, and monitor predictive or prescriptive models for use cases such as downtime, scrap, defects, bottlenecks, anomaly detection, yield, and preventive maintenance.

The Data Scientist applies statistics, machine learning, optimization, and programming to manufacturing data to improve safety, quality, throughput, cost, equipment reliability, and decision-making across plants. The role partners closely with Manufacturing IT, plant operations, engineering, quality, maintenance, and data engineering teams.

RESPONSIBILITIES

• Translate plant and business problems into measurable analytical questions and use cases.
• Identify, access, and assess data from MES, quality systems, equipment historians, maintenance systems, production systems, and other manufacturing sources.
• Build reliable analytical datasets and pipelines using SQL, Python, Spark, and Databricks.
• Perform exploratory analysis, statistical studies, root-cause analysis, forecasting, optimization, and experimentation.
• Develop, validate, document, and monitor predictive or prescriptive models for use cases such as downtime, scrap, defects, bottlenecks, anomaly detection, yield, and preventive maintenance.
• Evaluate data quality, lineage, coverage, missingness, bias, and operational readiness before modeling.
• Convert findings into practical recommendations that plant personnel and leaders can use in daily decisions.
• Create dashboards, visualizations, reports, and user interfaces that clearly communicate trends, risks, and opportunities.
• Productionize analytics and models in partnership with data engineering, application, and Manufacturing IT teams.
• Monitor model performance, data drift, pipeline health, and business impact after deployment.
• Support manufacturing modernization initiatives, including cloud migration, data-product development, automation, and legacy-system retirement.

REQUIREMENTS

• Bachelors Degree in a technical field such as computer science, computer engineering or related field required
• 8-10 years applicable experience required
• Data modeling at least 8-10 years of experience
• Data pipeline at least 8-10 years of experience
• Data analytics – able to build something out of messy data at least 8-10 years of experience
• Experience with database technologies
• Knowledge of the ETL process
• Knowledge of at least one scripting language
• Strong written and oral communication skills
• Strong troubleshooting and problem solving skills
• Demonstrated history of success
• Desire to be working with data and helping businesses make better data driven decisions

#LI-CH1