Locations: Dallas, TX | Frederick, MD | Joplin, MO | Phillipsburg, KS | Tuscaloosa, AL
My client is an industry-leading manufacturing organization that is investing heavily in AI, machine learning, advanced analytics, and digital transformation initiatives across its operations.
This is an opportunity for a hands-on Manufacturing Data Scientist to develop and deploy machine learning solutions that improve equipment reliability, product quality, process performance, and operational efficiency. You'll work directly with manufacturing stakeholders, leveraging plant-floor data to solve real-world business challenges while helping advance the organization's long-term vision for autonomous and data-driven manufacturing.
What You'll Be Doing
- Develop and deploy machine learning, predictive analytics, and anomaly detection solutions in manufacturing environments.
- Build models focused on predictive maintenance, forecasting, process optimization, equipment reliability, and quality improvement.
- Work with complex industrial datasets from sensors, historians, MES platforms, maintenance systems, quality systems, and other manufacturing data sources.
- Engineer features and transform noisy, real-world production data into actionable insights.
- Design and implement scalable data science solutions that move beyond proof of concept and into production.
- Partner with operations, engineering, maintenance, quality, and business stakeholders to identify opportunities and deliver measurable value.
- Apply statistical methods, process control techniques, and machine learning to reduce variation, improve process capability, and increase uptime.
- Support the development of next-generation AI initiatives, including generative AI, agentic workflows, and advanced analytics applications.
- Communicate findings and recommendations to both technical and non-technical audiences.
What We're Looking For
- Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field.
- 4+ years of experience in Data Science, Machine Learning, Advanced Analytics, or related disciplines.
- Strong experience with Python and SQL.
- Experience developing machine learning models on operational, manufacturing, industrial, or time-series datasets.
- Hands-on experience working with imperfect data sources, including sensor data, machine-generated data, and manually entered operational data.
- Understanding of statistical analysis, anomaly detection, predictive modeling, and data validation techniques.
- Experience deploying and maintaining models in production environments.
- Strong problem-solving abilities and the ability to work independently in fast-paced environments.
- Excellent communication skills and the ability to translate technical findings into business impact.
Preferred Experience
- Manufacturing, industrial, process manufacturing, automotive, chemical, consumer products, building materials, food & beverage, or similar industry experience.
- Predictive maintenance, reliability analytics, condition monitoring, or equipment health monitoring.
- MLOps tools and practices including MLflow, Docker, CI/CD, or model monitoring.
- Cloud platforms such as Azure or Databricks.
- Experience with historian platforms, OPC UA, MQTT, MES, SCADA, or other operational technology (OT) systems.
- Exposure to Generative AI, RAG, LLMs, agent frameworks, or modern AI engineering practices.
- Six Sigma, Statistical Process Control (SPC), or continuous improvement methodologies.