Data Scientist - Process Modeling & Machine Learning

SMS group•Pittsburgh, PA

1d•Hybrid

About The Position

We are seeking a Data Scientist who combines strong machine learning expertise with a genuine understanding of the Steel production processes behind the data. In this role, you will collaborate closely with process engineers and domain experts to understand how our machinery and production processes behave and apply that knowledge to develop models. The central focus of the position is enhancing our existing process models with data-driven techniques and machine learning. By integrating domain knowledge with advanced modeling methods, you will build solutions that perform robustly in production and earn the confidence of the engineers, operators, and customers who depend on them. At SMS group, our people are our greatest asset. We offer an entrepreneurial environment that promotes a culture of innovation, growth, and inclusion. We offer company events, activities, and opportunities to participate in charitable initiatives that benefit the communities where we are located. What you'll do: Design and implementation of digital solutions based on project requirements, SMS standards and similar reference projects Interfaces and communicate with individuals and units within or outside of the engineering department as well as with customers and suppliers, with responsibility for providing Data solutions on technical and project-related matters. Perform Big Data Solution programming based on the function descriptions for the equipment related to the project Design and develop big data architecture and Process Data warehouse. Participation in integration testing of the Data Solution with the control system before shipment to the job site On Site Commissioning and training of the Digital Solutions Service of existing systems as required

Requirements

Master’s degree in data science, Machine Learning, Statistics, Applied Mathematics, a quantitative engineering discipline (e.g., process, mechanical, control), or a related field or 2+ years of relevant experience.
Proven experience taking AI/ML solutions into real production environments (not just notebooks and prototypes).
Demonstrated ability to understand a problem domain and incorporate that understanding into models - comfort working alongside engineers and domain experts and learning the underlying process.
Understanding of software development practices: Python, SQL, Git, code review, and familiarity with container technologies.
Strong communication skills for working with other departments, customers, and stakeholders, and the ability to explain technical choices to non-specialists.
Ability to plan over longer horizons and coordinate work packages effectively.
Willingness to work on-site at the office and to travel to customer sites.
1-2 years of experience in applied artificial intelligence technologies including machine learning, predictive data analytics, or data science
BE/B. Tech or Advanced degree in a relevant field (Computer Science, Operations Research, Statistics, Mathematics, Electrical Engineering, or other Computational Science)
Demonstrated ability to iteratively conceptualize, design, and build data-driven analytical models
Some solid experience applying advanced AI techniques (machine learning, predictive analytics, optimization, semantic analysis, time-series analysis, advanced visualization) to real-world problems.
Strong capabilities in modern analytics languages/tools
Collaborative, open, and respectful working style
Passion for learning and a desire to grow
Be willing to travel to support the project work
Has experience manipulating and preparing large, heterogeneous data sets (“Big Data”) to support advanced analytics.
Excellent oral and written communication skills as well as excellent computer skills

Nice To Haves

Hands-on experience with hybrid / gray-box / physics-informed modeling, or with enhancing first-principles or simulation models using data-driven methods.
Experience building virtual/soft sensors, digital twins, or model-based monitoring for industrial or physical processes.
Proficiency with deep learning methods and frameworks.
Background in or exposure to an industrial / manufacturing / process domain (steel, metals, chemical, energy, or similar).
Experience researching and benchmarking existing solutions and algorithms before building from scratch.
Bring a particular depth in at least one of the following areas: Deep Learning, Optimization or Operations Research, NLP, Image Processing, or Machine Learning.

Responsibilities

Partner closely with process engineers, metallurgists, and domain experts to develop a deep, working understanding of the underlying processes and the data they generate.
Translate process know-how into model structure (constraints, features, and relationships) rather than treating the process as a black box.
Spend time where the data comes from: participate in site visits to connect raw signals to real physical behavior.
Enhance existing process models with data-driven techniques, replacing weak assumptions or unmodeled effects with learned components while preserving the physics that already works.
Design and implement hybrid models that combine domain/first-principles sub-models with machine learning (gray-box, physics-informed, and residual-modeling approaches).
Develop virtual/soft sensors to estimate quantities that are hard or expensive to measure directly.
Own data science problems end to end: scoping, data analysis (time-series and relational), feature engineering, modeling, validation, and deployment into production.
Serve as the algorithmic point of contact for your solutions, choosing the right tool for the problem, robust feature-based methods (e.g., scikit-learn) as well as deep learning (e.g., TensorFlow/Keras) where it adds value
Build engineering prototypes and turn promising experiments into reliable, maintainable production solutions
Monitor model performance on live data, diagnose drift, and retrain or recalibrate as conditions change.
Continuously optimize and maintain deployed solutions to improve accuracy, robustness, and runtime performance.
Work with cross-functional teams (product, engineering, project management) to align data science work with product roadmaps and project goals.
Engage with customers to gather feedback, refine solutions, and ensure that data-driven approaches are accepted and adopted by the people who use them.
Design and implementation of digital solutions based on project requirements, SMS standards and similar reference projects
Interfaces and communicate with individuals and units within or outside of the engineering department as well as with customers and suppliers, with responsibility for providing Data solutions on technical and project-related matters.
Perform Big Data Solution programming based on the function descriptions for the equipment related to the project
Design and develop big data architecture and Process Data warehouse.
Participation in integration testing of the Data Solution with the control system before shipment to the job site
On Site Commissioning and training of the Digital Solutions
Service of existing systems as required