Senior Data Scientist

Job reference

1281069

Location

Doha, Qatar

Sector

Energy - Oil & Gas

Function

IT & Telecoms

Employment type

Contract

Date published

September 29, 2026

Position Title: Senior Data Scientist

Job Purpose & Key Accountabilities

The Senior Data Scientist will play a leading role in shaping and executing the company's data science strategy, supporting its ambition to become a truly data-driven organisation. The position is responsible for driving innovation through advanced analytics, machine learning, generative AI, and agentic AI while delivering measurable business value across multiple operational and corporate functions.

Key Responsibilities

• Shape and deliver the data science strategy and execution roadmap in support of the company’s ambition to become a data-driven organisation.
• 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.
• Maximise 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.

Job Dimensions & Activities

Safety, Communication & Working Environment

• The role requires active contribution to a culture of safety, collaboration and continuous improvement while engaging effectively with both technical and non-technical stakeholders.
• Uphold and role model the company’s core values, incident-free culture and approved behaviours.
• Build team spirit, collaboration and a growth mindset across digital and business stakeholders.
• Communicate clearly with peers, leaders and business teams, translating complex data science concepts into practical business language.
• Mentor junior team members, provide technical guidance and contribute to the successful delivery of data science initiatives.

Study: Ideation & Experimentation

• Lead analytical innovation through exploration, experimentation and validation of advanced data science and AI solutions.
• Lead and build proof of concepts, prototypes, MVPs and use cases across advanced analytics, machine learning, big data, machine vision, generative AI and agentic AI.
• Conduct data science studies through robust quantitative analysis, statistical modelling, experimentation and validation.
• Develop analytical solutions that enable in-house use case development and business decision support.
• Translate analytical findings into actionable recommendations, measurable outcomes and clear implementation options.

Use Case: Envisioning & Conceptualisation

• Collaborate with business stakeholders to identify, frame and prioritise data science opportunities with strong business value.
• Frame use cases with business stakeholders by assessing value, feasibility, data readiness, risks and the most appropriate analytical or AI methodology.
• Contribute to technology choices, platform strategy, solution architecture and prioritisation of data science opportunities under the leadership of the Lead Data Scientist.
• Create technical specifications, model requirements, data requirements and integration needs for the technical scope of work.
• Understand enterprise data sources, perform queries and translate business requirements into scalable data-driven solutions.
• Support market scouting, vendor engagement and Call for Tender technical evaluations, including assessment of vendors’ data science, AI and delivery capabilities.

Use Case: Development

• Provide technical leadership and oversight throughout the end-to-end development lifecycle of analytics and AI solutions.
• Supervise, challenge and provide guidance on vendor-led and in-house data analytics, data science, AI and machine learning delivery.
• Coordinate with development leads, product owners, Scrum teams and business SMEs to resolve issues and maintain delivery momentum.
• Oversee the e2e design and implementation of analytics models by the vendors, ensuring the required quality.
• Contribute to MLOps practices including model versioning, deployment readiness, monitoring, retraining, performance tracking and drift management.
• Support user acceptance testing and technical validation of models, dashboards, AI assistants and analytical products.
• Explain sophisticated data science concepts in an understandable manner.

Use Case: Roll-Out & Adoption

• Support successful deployment and user adoption of data science solutions across the organization.
• Promote adoption of data science products by technical departments and ensure users understand the value, limitations and correct use of delivered solutions.
• Support transition from experimentation to project or use case mode, including deployment at scale and integration into business workflows.

Use Case: Enhancements

• Ensure the continuous improvement and sustainability of deployed data science solutions.
• Support model retraining, recalibration, enhancement based on changing data, user feedback and business needs.
• Monitor new analytical methods, AI techniques, software, hardware and platform trends relevant to the business.
• Contribute to partnerships, standards and reusable methods that improve the maturity and sustainability of data science delivery.

Context & Work Environment

• Position located in Doha, Qatar.
• Standard office hours.
• Five working days per week.

Qualifications, Experience & Skills

• Education: Master’s degree in a relevant field such as Data Science, Computer Science, Statistics, Mathematics, Engineering or a related quantitative discipline.

Must Have Experience

• 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.
  • 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.
  • 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
    • Model governance
• Cloud & Platforms
  • Hands-on experience with Microsoft Azure.
  • Experience with Azure AI/Data Services, Databricks, lakehouse platforms or equivalent cloud-native data and AI services is an advantage.
• Visualisation & Storytelling
  • Proficiency in Power BI or similar visualisation tools.
  • Ability to communicate insights, uncertainty, assumptions and recommendations to both 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.
  • Ability to build trust and influence decisions through clear and transparent communication.

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