Artificial Intelligence Scientist

🔥 15 minutes ago

🇧🇷 Brazil – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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ANDRITZ

WebsiteLinkedInAll Job Openings

10,000+ employees

🚀 Aerospace

🏭 Manufacturing

Environment & Energy • Aerospace • Manufacturing

ANDRITZ is a globally leading supplier of process technologies, equipment, plants, and automation solutions. The company focuses on enabling the green transition through sustainable industrial transformation across multiple sectors including pulp and paper, metals, hydropower, and environmental and energy solutions. With a commitment to innovation and decarbonization, ANDRITZ's comprehensive portfolio includes technologies for recycling, renewable energy, and various industrial applications, contributing to a more sustainable world.

📋 Description

• 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

• 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