Senior Data Scientist

Overview

The Senior Data Scientist role is pivotal in steering and implementing the organization's data science vision to establish itself as a data-centric entity. The position focuses on leveraging advanced analytics, machine learning, generative AI, and agentic AI to generate significant business impact across various operational and corporate segments.

Key Duties

• Develop and execute a comprehensive data science roadmap aligning with the company’s data-driven goals.
• Advocate the use of advanced machine learning techniques, generative and agentic AI to enhance insights, operational efficiency, safety, and decision processes.
• Lead creation of high-value proofs of concept, minimum viable products, and scalable AI solutions ensuring tangible business benefits and user adoption.
• Optimize value extraction from diverse enterprise data including geoscience, field operations, HSE, corporate planning, major projects, and engineering through robust analytics and modeling.
• Oversee collaboration with internal stakeholders and external suppliers to guarantee technically sound, secure, production-ready data science implementations.

Work Environment & Culture

• Champion a safety-first mindset and foster a culture of collaboration and continuous progress engaging both technical and business teams.
• Act as a role model for organizational core values and incident-free operating behavior.
• Encourage team cohesion, cooperation, and a growth-oriented attitude among digital and business collaborators.
• Effectively communicate complex data science notions in accessible business terms to peers and leadership.
• Mentor junior colleagues providing technical support and facilitating successful project delivery.

Innovation and Experimentation

• Drive innovation through prototyping, experimentation, and validation of cutting-edge AI and data science approaches including big data, machine vision, generative and agentic AI.
• Conduct thorough quantitative assessments, statistical modeling, and experimental validations.
• Design analytical solutions empowering internal use case development and aiding business decisions.
• Translate analytical outcomes into actionable strategies with measurable results.

Use Case Identification and Development

• Partner with business units to identify, formulate, and prioritize high-impact data science opportunities.
• Evaluate feasibility, data preparedness, risks, and select fitting analytical or AI methods for use cases.
• Support technology decisions, platform planning, and define solution architectures under Lead Data Scientist guidance.
• Draft technical and data specifications, including integration requirements for data science initiatives.
• Query and integrate enterprise data sources aligning with business needs.
• Assist in supplier evaluations and technical tender assessments related to AI and data science capabilities.

Solution Development and Management

• Provide technical leadership throughout the full development lifecycle of AI and analytics solutions.
• Guide and challenge vendor and internal teams delivering data science projects.
• Coordinate with product owners, Scrum teams, and subject matter experts to maintain progress and solve issues.
• Ensure thorough design and high quality of vendor-developed analytics models.
• Contribute to MLOps best practices including versioning, deployment, monitoring, retraining, and drift detection.
• Support acceptance testing and validation of models, dashboards, AI tools, and analytics products.
• Communicate advanced data science concepts clearly and understandably.

Deployment and Adoption

• Facilitate effective deployment and organizational uptake of data science outputs.
• Encourage adoption by technical teams ensuring awareness of solution capabilities and limitations.
• Support transition from experimental to production mode including scaling and integration into workflows.

Ongoing Enhancements

• Maintain continuous improvement and sustainability of data science deployments.
• Manage model updates, recalibration and enhancements driven by data changes, user feedback, and evolving business needs.
• Stay informed on emerging analytical methods, AI trends, and relevant technologies.
• Contribute to efforts enhancing data science delivery maturity, standards, and reusable methodologies.

Additional Information

• Work location: Doha, Qatar.
• Standard office hours with a five-day workweek.
• Master’s degree required in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.

Experience Requirements

• At least eight years in data science, applied AI, or advanced analytics with demonstrable success in model development, validation, and deployment.
• Proven ability to translate business challenges into analytics use cases with measurable outcomes.
• Prior experience in oil and gas or industrial sectors with familiarity in operational, geoscience, production, reservoir, HSE, and engineering data.
• Preferred experience with generative AI, LLMs, retrieval-augmented generation, prompt orchestration, and agentic AI workflows.
• Expertise in machine learning lifecycle management covering experimentation, validation, deployment, monitoring, retraining, and model governance.

Technical Competencies

• Proficient in Python programming; experience with Spark or distributed computing advantageous.
• Strong SQL and Pandas capabilities for data processing and preparation.
• Knowledge of data pipelines, data quality assurance, metadata handling, and processing structured and unstructured datasets.
• Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, and XGBoost.
• Familiarity with production-grade ML practices including version control, model tracking, continuous integration, APIs, performance monitoring, and governance.
• Hands-on experience with Microsoft Azure cloud services and Azure AI/Data platforms preferred.
• Ability to utilize visualization tools like Power BI to effectively communicate insights to diverse audiences.
• Strong problem-solving skills with capability to resolve ambiguous business problems using scalable data science solutions.
• Excellent stakeholder management and cross-functional collaboration skills to foster trust and influence decisions via transparent communication.