AI/ML Scientist

Scope of Position

Hatch develops digital and AI solutions across the Metals, Energy, and Infrastructure sectors to enable operational safety, efficiency, value chain optimization, and real-time process control and automation. Our clients are accelerating investments in data platforms, industrial IoT, cloud-native architectures, IT/OT integration, and AI-enabled applications to unlock real-time insights and improve operational performance.

Hatch is seeking a highly motivated AI/ML Scientist to join our Digital team in Katowice Poland. Reporting to the Global Director of Analytics and Decision Solutions, this role will play a critical part in piloting, developing, deploying, and scaling advanced analytics and AI solutions across industrial operations and enterprise value chains.

The successful candidate will be responsible for:

Model Development and Deployment

• Design, develop, validate, and deploy AI/ML models for industrial applications, including predictive maintenance, process modeling, control and optimization, anomaly detection, forecasting, computer vision, and prescriptive analytics.

• Apply advanced AI and machine learning methodologies, including supervised and unsupervised learning, deep learning, reinforcement learning, optimization techniques, and large language models (LLMs), where appropriate, to solve complex industrial challenges.

• Develop robust data pipelines, feature engineering workflows, and analytical frameworks to transform raw operational and process data into actionable insights and production-ready solutions.

• Translate complex analytical findings into practical recommendations aligned with operational goals and business objectives.

• Collaborate closely with process engineers, data engineers, control specialists, and IT/OT teams to deploy AI and machine learning solutions into production environments and integrate models with enterprise systems, operational platforms, dashboards, and decision-support tools.

Delivery Excellence and Technology Development

• Partner with clients and internal teams to identify high-value AI opportunities and quantify business impact and value potential.

• Conduct data-driven opportunity assessments, proof-of-value (PoV) studies, and rapid prototyping initiatives to demonstrate measurable business outcomes and communicate insights effectively to internal and external stakeholders.

• Support the end-to-end AI lifecycle, from problem definition and data exploration through model development, validation, deployment, monitoring, and continuous improvement.

• Contribute to the growth of Hatch’s AI and advanced analytics capabilities through the development of reusable frameworks, accelerators, methodologies, and best practices.

• Stay current with emerging trends and technologies in artificial intelligence, machine learning, optimization, and industrial analytics, bringing innovative approaches to client challenges.

• Contribute to technical publications, conference presentations, white papers, and internal knowledge-sharing initiatives.

Experience and Skills Important for the Role:

• Bachelor’s or advanced degree in Engineering, Data Science, Applied Mathematics, Operations Research, or related field. A degree in Chemical Engineering, Metallurgical Engineering, Mining Engineering, or a related discipline with a specialization in Process Control, Advanced Process Control (APC), Optimization, or Industrial AI is considered a significant asset.

• Strong proficiency in Python, including experience with scientific and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, SciPy, Matplotlib, and related tools.

• Solid understanding of machine learning and statistical modeling concepts, including time-series analysis, feature engineering, optimization, experimentation, and model evaluation techniques.

• Experience applying advanced analytics, AI, and machine learning techniques within mining and metals, energy, infrastructure, manufacturing, or other asset-intensive industrial sectors. Experience with computer vision, Vision AI, and LLM/LVM applications is considered a strong asset.

• Experience working with industrial-scale datasets, including process historian data, sensor data, operational datasets, and time-series data.

• Working knowledge of data engineering and MLOps concepts, including SQL, APIs, ETL/ELT workflows, cloud platforms (Azure, AWS, or GCP), and model deployment practices.

• Strong communication and collaboration skills, with the ability to work effectively across multidisciplinary teams and rapidly learn and apply new technologies and concepts.

• Ability to frame ambiguous business challenges into structured analytical approaches, and communicate complex technical findings effectively to technical and non-technical audiences.

The following experience and skills are considered strong assets:

• Experience operationalizing AI/ML solutions using modern data platforms and MLOps practices (e.g., Databricks, MLflow, etc.)

• Familiarity with industrial IT/OT environments, including data historians (e.g., AVEVA PI System), real-time data integration, control systems, instrumentation, P&IDs, and industrial cybersecurity considerations.

• Multilingual capability in English, Spanish, and/or Portuguese, particularly for collaboration across global project teams and client operations.