Machine Learning Engineer

Machine Learning Engineer (Energy Sector Focus)

Our client is seeking a highly skilled and experienced Machine Learning Engineer to join their data science and AI team. This role is critical for leveraging cutting-edge machine learning and AI techniques to optimise operations, enhance exploration and production efficiency, drive the energy transition and improve decision-making across the organisation. The successful candidate will have a strong foundation in ML engineering principles and demonstrated prior experience working within the energy, oil, and gas, or a related industrial sector.

Key Responsibilities

• Design, develop, and deploy robust, scalable, and production-ready machine learning models and pipelines for various energy-sector applications
• Collaborate with domain experts (geoscientists, reservoir engineers, operational technologists) to understand complex business problems and translate them into actionable ML solutions.
• Build and maintain the necessary infrastructure for model training, versioning, deployment, and monitoring (MLOps).
• Conduct rigorous data exploration, cleaning, and feature engineering on large, complex, and often sparse energy-related datasets.
• Evaluate and optimize model performance, ensuring high accuracy, reliability, and interpretability in a high-stakes operational environment.
• Stay current with the latest advancements in machine learning, deep learning, and MLOps to continuously improve AI capabilities.
• Ensure compliance with data privacy, security, and operational safety standards.

Essential Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.
• Minimum 3+ years of professional experience as an ML Engineer, Data Scientist, or in a similar role.
• Demonstrable and significant prior experience (2+ years) working specifically within the energy, oil & gas, utilities, or a heavy industrial sector where data science was applied to core operational or strategic challenges.
• Proficiency in designing, implementing, and maintaining MLOps processes in a cloud environment (e.g., Azure, AWS, GCP).

Technical Skills:

• Expertise in Python and its ML ecosystem (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
• Strong background in statistical analysis, algorithm design, and software engineering best practices.
• Experience with Docker and Kubernetes for containerization and orchestration.
• Proficiency with modern version control systems (Git).
• Familiarity with common data sources and types within the energy sector (e.g., SCADA data, seismic data, well logs, sensor data from IoT devices, real-time operational metrics).

Desirable Skills (Nice to Have)

• Experience with Microsoft Azure and services like Azure Machine Learning.
• Knowledge of time-series analysis and spatio-temporal modeling techniques.
• Familiarity with geospatial data processing and visualization.
• Experience contributing to open-source ML projects or publishing technical papers.
• Strong verbal and written communication skills for technical and non-technical audiences.