Senior Data Scientist

Your tasks

• Shape and deliver the data science strategy and execution roadmap in support of the company's ambition to become a data-driven organization.
• Promote advanced analytics, machine learning, generative AI and agentic AI to improve business insight, operational performance, safety, efficiency and decision-making.
• Lead the development of high-value PoCs, MVPs and scalable data science solutions, ensuring clear business impact and adoption by end users.
• Maximize the value of geoscience, field operations, HSE, corporate planning, major projects & engineering and other enterprise data through robust analytics, modelling, data products and decision-support solutions.
• Manage internal stakeholders and external vendors to ensure technically sound, production-ready, secure and governable data science delivery.

Requirements

Education Master's degree in a relevant field such as data science, computer science, statistics, mathematics, engineering or a related quantitative discipline.

• Minimum 8 years of experience in data science, advanced analytics and/or applied AI, with a proven track record of developing, validating and implementing machine learning models and data-driven decision solutions.
• Experience translating business problems into analytical use cases, defining success metrics, assessing data readiness and delivering measurable business value.
• Industry experience in oil and gas or industrial operations, with understanding of operational, geoscience, production, reservoir, HSE and engineering data.
• Hands-on experience with generative AI, LLM-based solutions, retrieval-augmented generation, prompt orchestration, and agentic AI workflows is an advantage.
• Experience with the machine learning lifecycle, including experimentation, validation, deployment readiness, monitoring, retraining, drift management and enhancement.

Technical & soft skills

• Programming: Strong proficiency in Python is required; experience with Spark or distributed processing is an advantage.
• Data processing: Strong experience with SQL, Pandas and data preparation, with exposure to data pipelines, data quality, metadata and structured or unstructured data processing.
• Machine learning: Experience with Scikit-learn, TensorFlow, PyTorch, XGBoost or similar frameworks, with strong understanding of statistical modelling, predictive analytics, validation and experimentation.
• MLOps: Familiarity with production-ready ML practices such as version control, model tracking, CI/CD concepts, APIs, monitoring, retraining, drift detection and model governance.
• Cloud and platforms: Hands-on experience with Microsoft Azure, Azure AI/data services, Databricks, lakehouse platforms or equivalent cloud-native data and AI services is an advantage.
• Visualization and storytelling: Proficiency in Power BI or similar visualization tools, with ability to communicate insights, uncertainty, assumptions and recommendations to technical and non-technical audiences.
• Problem-solving: Proven ability to structure ambiguous business challenges and convert them into practical, scalable and value-adding data science solutions.
• Collaboration: Strong stakeholder management, vendor coordination and cross-functional collaboration skills, with the ability to build trust and influence decisions through clear, transparent communication.

Language proficiency Mastery of English across spoken, written and active listening skills, enabling clear, empathetic and effective communication in diverse professional settings.