Senior Data Scientist

Role purpose

Strategy and innovation

• Shape the data science strategy and execution roadmap and promote analytics and AI for business insight, performance, safety, efficiency, and decision-making.
• Lead proofs of concept, prototypes, MVPs, studies, experiments, and scalable solutions across advanced analytics, machine learning, big data, machine vision, generative AI, and agentic AI.
• Use quantitative analysis, statistical modelling, experimentation, and validation to develop decision-support solutions and actionable recommendations.

Use-case development and delivery

• Collaborate with business stakeholders to identify, assess, frame, and prioritize use cases based on value, feasibility, data readiness, risk, and appropriate methods.
• Define technical specifications, model and data requirements, integration needs, solution architecture, and scalable data-driven approaches.
• Guide in-house and vendor delivery; oversee model design and implementation quality and coordinate with development leads, product owners, Scrum teams, and business SMEs.
• Contribute to MLOps, including versioning, deployment readiness, monitoring, retraining, performance tracking, and drift management; support testing and validation of analytical products.

Rollout and improvement

• Support solution deployment, user adoption, transition from experimentation to scaled projects, and integration into business workflows.
• Improve deployed solutions through retraining and recalibration, monitor relevant technology trends, and contribute reusable methods and standards.

Collaboration and leadership

• Manage stakeholders and vendors and support technical evaluations and market scouting.
• Mentor junior team members, provide technical guidance, and explain complex data science concepts in practical business language.
• Promote safety, collaboration, continuous improvement, team spirit, and a growth mindset.

Education and experience

• Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.
• At least 8 years of data science, advanced analytics, and/or applied AI experience, including developing, validating, and implementing machine-learning models and decision solutions.
• Experience translating business problems into use cases, defining success metrics, assessing data readiness, and demonstrating measurable business value.
• Oil and gas or industrial operations experience, with understanding of operational, geoscience, production, reservoir, HSE, and engineering data.

Technical skills

• Strong proficiency in Python; strong experience with SQL, Pandas, and data preparation.
• Experience with Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar; strong statistical modelling, predictive analytics, validation, and experimentation knowledge.
• Experience with the machine-learning lifecycle, including deployment readiness, monitoring, retraining, and drift management.
• Hands-on Microsoft Azure experience; Power BI or similar visualization proficiency.
• Experience with generative AI, LLM solutions, retrieval-augmented generation, prompt orchestration, and agentic AI workflows is advantageous.
• Spark or distributed processing and Azure AI/Data Services, Databricks, lakehouse platforms, or equivalent services are advantageous.
• Familiarity with production ML practices such as version control, model tracking, CI/CD, APIs, monitoring, retraining, drift detection, and governance.

Collaboration skills

• Communicate insights, uncertainty, assumptions, and recommendations to technical and non-technical audiences.
• Demonstrate stakeholder management, vendor coordination, cross-functional collaboration, and the ability to structure ambiguous challenges into practical solutions.

Work environment

• Position located in Doha, Qatar, with standard office hours across five working days per week.