As a Lead Data Scientist, you will spearhead advanced analytics initiatives, leveraging data-driven insights to optimize exploration, production, and operational efficiency. Your role involves building predictive models, deploying machine learning algorithms, and leading a team to solve complex challenges unique to the industry.

This opportunity includes a four-month, all-expenses-paid international immersion in Abu Dhabi, at the heart of the Oil & Gas sector. You’ll learn on-site from industry experts, gain hands-on exposure to real field data and operations, and expand your global perspective—flights, housing, and local support included—while delivering high-impact solutions you can later scale across the organization.

Technical Responsibilities: 

1. Data Strategy and Management: 

• Define and implement data strategies to support exploration, drilling, and production business goals. 

• Oversee data collection, cleaning, and integration from diverse sources (e.g., seismic, production logs, IoT sensors, SCADA systems). 

• Ensure data accuracy, consistency, and security while adhering to industry compliance standards. 

2. Model Development and Deployment: 

• Design and implement advanced machine learning models (e.g., predictive maintenance, reservoir simulations, production optimisation). 

• Develop algorithms for seismic data interpretation, reservoir characterisation, and well-performance forecasting. 

• Optimize workflows using natural language processing (NLP) for unstructured data, such as drilling reports and maintenance logs. 

3. Advanced Analytics: 

• Conduct exploratory data analysis (EDA) to identify trends, anomalies, and optimisation opportunities. 

• Utilize geospatial analysis and geostatistical techniques to interpret geological and geophysical data. 

• Implement real-time data analytics for drilling, well monitoring, and production enhancement. 

4. Technology Leadership: 

• Lead the adoption of cloud-based data platforms (e.g., Azure, AWS, Google Cloud) for scalable computation. 

• Drive automation of repetitive tasks using advanced scripting and machine learning pipelines. 

5. Team Collaboration and Leadership: 

• Mentor junior data scientists and engineers, fostering a culture of innovation and excellence. 

• Collaborate with engineers, geophysicists, and reservoir managers to translate business challenges into data science solutions. 

• Communicate technical findings to non-technical stakeholders through visualisations and reports. 

6. Optimization and Decision Support: 

• Develop optimisation models for energy efficiency, cost reduction, and supply chain logistics. 

• Enhance drilling accuracy and reduce downtime through predictive analytics for equipment maintenance. 

• Implement risk assessment models to improve safety and compliance standards. 

7. Tool and Technology Expertise: 

• Expertise in Python, R, MATLAB, and SQL for statistical modelling and data analysis. 

• Proficient in machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and big data tools (e.g., Hadoop, Spark). 

• Hands-on experience with visualisation tools like Power BI, Tableau, or D3.js. 

• Familiarity with domain-specific software like Petrel, Schlumberger, or Halliburton’s DecisionSpace. 

Qualifications: 

• Advanced degree (Master’s or PhD) in Data Science, Petroleum Engineering, Geophysics, Computer Science, or a related field. 

• 7+ years of experience in data science, with at least 3 years in oil and gas. 

• Strong understanding of petroleum systems, reservoir engineering, and upstream/downstream operations. 

• Demonstrated success in leading data-driven projects within the oil and gas sector. 

Key Skills: 

• Deep knowledge of machine learning, statistical modelling, and geostatistics. 

• Strong programming and data engineering skills. 

• Understanding of the oil and gas lifecycle, from exploration to production. 

• Excellent problem-solving and communication abilities.