Data Scientist

Must have experience with data!

This is a true Data Scientist role

Application AI platform skill set is a nice to have, not 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

Role summary

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.

Key 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.

Present technical results to both technical and nontechnical audiences and maintain clear documentation.

Promote reusable analytical methods, standards, and best practices across plants and manufacturing domains.

Education - Bachelor's degree in a technical field such as computer science, computer engineering or related field required

Years of experience – at least 8-10 years of experience