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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in mathematical optimization, machine learning, and artificial intelligence solutions tailored for industrial processes. Proficient in data preparation, feature engineering, and the development of model training and deployment pipelines.
Highest-signal resume keywords
Mathematical OptimizationMachine LearningPython ProgrammingData PreparationFeature Engineering
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Linear ProgrammingMixed-Integer ProgrammingNonlinear ProgrammingConstraint ProgrammingSupervised LearningUnsupervised LearningNeural NetworksModel ValidationModel EvaluationData Science
Soft Skills
Clear CommunicationInterpersonal SkillsCollaborationAnalytical MindsetResults Orientation
Tools & Technologies
GurobiCPLEXOR-ToolsPyomoNumPyPandasScikit-learnPyTorchC#/.NETSQL
Industry Keywords
Process AutomationIndustrial ProcessesLLM/RAG AgentsReinforcement LearningMetaheuristicsBayesian OptimizationMLOps
Tech Stack
Tools & technologiesNumpyPandasPythonPyTorchScikit-LearnSQL.NET
About the role
Key responsibilities & impact- Translate industrial and process-related problems into mathematical optimization, machine learning, or artificial intelligence solutions
- Formulate and solve mathematical optimization problems, including linear, mixed-integer, nonlinear, and constraint programming
- Design and develop machine learning models applied to the control and operation of industrial processes
- Develop solutions based on historical process data to automatically improve the performance of control loops and control strategies
- Develop LLM-based artificial intelligence agents to support engineering activities and the operation of control systems
- Prepare process data, including collection, cleaning, synchronization, and the handling of time series, outliers, and missing data
- Develop feature engineering processes for model training
- Define and implement model training, validation, and deployment pipelines with software and data teams
- Support the integration of algorithms into the area's products in collaboration with the software development team
- Conduct proof-of-concept projects for algorithms and solutions in real-world plants
- Prepare configuration and usage documentation for non-specialist professionals
- Research new optimization, machine learning, and artificial intelligence technologies for current and future products
Requirements
What you’ll need- Advanced English proficiency
- Ability to translate industrial and process-related problems into optimization, machine learning, and artificial intelligence solutions
- Strong knowledge of mathematical optimization and problem formulation, including linear programming, mixed-integer programming, nonlinear programming, and constraint programming
- Experience with solvers and modeling languages such as Gurobi, CPLEX, OR-Tools, or Pyomo
- Strong knowledge of machine learning, including supervised and unsupervised learning, neural networks, model validation, and model evaluation
- Experience in data preparation and feature engineering, with an emphasis on time series processing and modeling
- Understanding of data pipelines for model training, validation, and deployment
- Proficiency in Python and data science, machine learning, and optimization libraries such as NumPy, pandas, scikit-learn, and PyTorch
- Clear and objective communication skills, with the ability to work in multicultural teams
- Willingness and ability to continuously learn and stay current with innovations and trends
- Strong interpersonal and collaboration skills, with the ability to work smoothly and effectively as part of a team, either on-site or remotely
- Systems and strategic thinking, analytical mindset, results orientation, discipline, and innovation
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field
- Preferred qualifications: process automation and control, industrial processes, C#/.NET, Git, SQL, LLM/RAG agents, reinforcement learning, metaheuristics, Bayesian optimization, and MLOps practices