Level 3 Data Scientist

• Collaborate with business users, internal stakeholders and Digital teams to apply data science techniques to deliver value across the oil and gas value chain, understanding and shape client requirements, and translating them into Data Science actions
• Contribute to data-driven data science project from end-to-end (i.e. problem identification to delivery)
• Evaluate and apply data science concepts and techniques (e.g. predictive modelling, statistical inference, algorithms) to problems in oil and gas exploration and production domains
• Communicate key assumptions, uncertainties, findings of statistical or technical analysis through reports, presentations and translate results back into business language
• Utilizing machine learning, artificial intelligence, and data visualization techniques to identify trends, patterns, and anomalies in oil and gas data
• Validate models using statistical and scientific methodologies, ensuring robustness, reproducibility, and interpretability
• Develop predictive models, time-series forecasting methods, anomaly detection pipelines, clustering algorithms, and OCR or neural-network models as required
• Collaborate with SMEs on analysis and modelling processes to solve challenging and high-impact problems in oil and gas exploration and production domains
• Create documentation, develop and improve data science processes and best practices to ensure the sustainability of work
• Contribute to successful implementation of solutions, ensuring training and knowledge transfer to stakeholders
• Deliver data science solutions from opportunity identification through to handover, including planning, experimentation, implementation, and monitoring

• Min. 3-year proven practical experience in Data Science across the end-to-end model development lifecycle, with machine learning and AI methods
• Strong Python programming skills and experience with ML libraries (Pandas, NumPy, SciPy, scikit-learn, PyTorch/TensorFlow)
• Demonstrated expertise in time-series analysis, anomaly detection, clustering, and general applied machine learning
• Experience developing web applications (Dash, Streamlit) and APIs (Flask, FastAPI, or Django)
• Experience with software engineering best practices (e.g. CI/CD principles under Agile Framework, version control, reproducible research, etc.)
• Strong competency using AWS cloud services, including S3, Lambda, SageMaker, containerisation, and serverless execution
• Strong documentation, communication, and collaboration capabilities
• Strong problem-solving and critical thinking skills
• Excellent documentation skills to ensure the sustainability of work
• Quantitative analytics
• Excellent written and verbal communication
• Tertiary qualification in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a similar quantitative discipline

Desirable experience being part of a multi-disciplinary team for Digital solution delivery or Experience with Cloud Technologies (AWS, MS Azure)