Industrial Process ML Scientist - Hybrid Modeling

Arkansas Northeastern College; Pittsburgh (Allegheny County); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Open culture

Professional development

Real-world impact

Continuous learning

Industry standards contribution

Job summary

SMS group is seeking a Data Scientist to blend machine learning with domain knowledge of manufacturing processes. You will partner with process engineers to extend model-based systems, build digital twins and soft sensors, and deploy robust data‑driven solutions into production.

You will own scoping, analysis, feature engineering, modeling, validation, and deployment, while monitoring performance and collaborating with product, engineering, and customers to drive impact.

Qualifications

• Master’s degree in data science, ML, statistics, or related field or 2+ years of experience.
• Proven production experience deploying AI/ML solutions.
• Ability to understand problems with engineers and domain experts and translate to models.
• Proficiency in Python, SQL, Git and container tech.

Responsibilities

• Scope data science problems end to end: data analysis, feature engineering, modeling, validation and deployment.
• Serve as algorithmic lead for solutions, selecting appropriate tools and methods.
• Build prototypes and convert experiments into robust production systems.
• Monitor model performance, detect drift and retrain as needed; collaborate with cross‑functional teams.

Skills

Python

SQL

Git

Docker

Communication

Planning

Education

Master's degree in data science or related field

2+ years of relevant experience

Tools

scikit-learn

TensorFlow/Keras

Docker

Job description

SMS group is seeking a Data Scientist to blend machine learning with domain knowledge of manufacturing processes. You will partner with process engineers to extend model-based systems, build digital twins and soft sensors, and deploy robust data‑driven solutions into production.

You will own scoping, analysis, feature engineering, modeling, validation, and deployment, while monitoring performance and collaborating with product, engineering, and customers to drive impact.