Data Scientist

Overview

Develop and deliver AI/ML solutions for energy analytics platform

Key Responsibilities

• code development
• generative ai
• ai modelling
• ml techniques
• model deployment
• requirement collaboration

Tasks

-Participate in peer reviews, knowledge sharing sessions, and technical discussions, while continuously developing your own skills and knowledge, and receiving mentorship from senior colleagues -Write maintainable, testable, and optimised code, and contribute to the continuous improvement of our data science practices -Leverage Generative AI capabilities, including LLM post-training, to develop innovative solutions for energy-related challenges -Design, develop, and evaluate AI and Machine Learning models -Apply cutting-edge Machine Learning techniques to solve complex modelling challenges in the energy sector -Support the delivery of analytical components from concept to deployment, working closely with other team members to validate approaches and ensure quality outcomes -Collaborate with product, engineering, and domain teams to understand user requirements and develop innovative, production-ready AI solutions aligned with strategic objectives

Requirements

• mlops
• python
• aws
• tensorflow
• git
• technical degree

What You Bring

-MLOps experience: Knowledge and familiarity with MLOps frameworks and tools such as Sagemaker, Kedro, MLflow or Weights and Biases -We run our ETL data pipelines using Python -You hold a degree in a technical or quantitative field (e.g., AI, computer science, engineering, mathematics, physics, or related discipline) with proven professional experience applying data science in commercial or research environments -Advanced ML Architectures: Experience with implementing specialised neural networks such as Graph Neural Networks (GNN) for modelling complex relationships, Physics-Informed Neural Networks (PINN) for incorporating domain knowledge, or Temporal Fusion Transformers (TFT) for advanced and interpretable forecasting -We implement GraphQL and RESTful APIs using NodeJS and Python -Energy Domain Knowledge: Background in power systems, energy dispatch optimisation, grid modelling, or other energy sector applications where AI/ML drives operational decisions -DynamoDB, Redshift, Postgres, Opensearch, and S3 are our go to data stores -You excel in collaborative team environments while taking full ownership of your deliverables -Practical experience with Generative AI and exposure to leading LLM platforms (Anthropic, Meta, Amazon , OpenAI) -Strong skills in data preprocessing, wrangling, and augmentation techniques -Proficiency with essential data science libraries including Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks -You possess strong analytical skills, demonstrate attention to detail, and excel at transforming data into actionable insights -You communicate effectively with both technical and non-technical stakeholders and thrive in collaborative, cross-functional environments -Proficiency with version control systems including Git and GitHub -Experience deploying scalable AI solutions on cloud platforms (AWS, Google Cloud, or Azure) with enthusiasm for MLOps tools and practices -You demonstrate intellectual curiosity, embrace continuous learning, and are passionate about advancing your expertise in data science and AI -Knowledge of ML-adjacent technologies, including AWS SageMaker, Kedro and MLflow. -Strong Python proficiency with hands-on experience in AI/ML frameworks including RAG, LangChain, TensorFlow, and PyTorch -LLM Specialisation: Hands-on experience with modern LLM training techniques including fine-tuning, RLHF, parameter-efficient methods (LoRA/QLoRA), or custom post-training workflows -You have successfully applied Machine Learning and Generative AI techniques in real-world projects, delivering innovative products to market -Our web products are developed using TypeScript, React, and Redux -You have experience with version control systems, agile methodologies, and collaborative development environments -Our backend services are implemented in C# / .NET or Typescript / NodeJS

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About Wood Mackenzie

-Pivoted in 1973 to pioneering energy research with its first oil report. -Over five decades evolved into a global consultancy powering decisions in energy, chemicals, metals, mining and renewables. -Its Lens platform spans power, hydrogen, carbon, LNG, maritime and more—turning vast datasets into strategic foresight. -Typical projects include asset valuation, project economics, supply‑chain intelligence and portfolio optimization. -With 30+ offices and a presence across energy value chains, it guides governments, producers and financial institutions. -Stands out by integrating legacy upstream expertise with cutting‑edge analytics across renewables and transition fuels. -Notable for weaving real‑time vessel tracking and carbon insights alongside decades‑deep commodity research.

Sector Specialisms