Machine Learning Engineer
Posted 6 days ago by SearchWorks
Negotiable
Undetermined
Undetermined
England, United Kingdom
Summary: The Machine Learning Engineer role focuses on applying advanced machine learning and AI techniques within the energy sector to optimize operations and enhance decision-making. The position requires collaboration with domain experts to develop scalable ML models and maintain the necessary infrastructure for model deployment and monitoring. Candidates should have a strong background in ML engineering and relevant industry experience. This role is pivotal in driving the energy transition and improving operational efficiency.
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.
Key Skills:
- 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).
- 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).
Salary (Rate): undetermined
City: undetermined
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: Energy
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.