Senior Data Scientist on-site

Senior Data Scientist (Abu Dhabi, on-site)

HOT

We are looking for a hands-on Senior Data Scientist to develop production-ready predictive and optimization models for a complex aviation fuel-efficiency initiative. The work will combine multiple operational factors to support better predictions, detect meaningful patterns or anomalies, and identify opportunities for fuel optimization.

The right person combines strong theoretical foundations with the practical judgment required to turn imperfect real-world data into models that can be deployed, monitored, and improved in production.

• Translate operational questions into well-defined prediction, detection, segmentation, forecasting, and optimization problems.
• Develop models that use multiple operational factors to support fuel-optimization decisions.
• Select and apply appropriate methods, including gradient boosting, clustering, time-series modeling, prediction and detection techniques, and mathematical or heuristic optimization.
• Establish credible baselines, design validation and back-testing approaches, and choose metrics that reflect operational value.
• Explain model behavior, assumptions, limitations, and trade-offs clearly to technical and operational stakeholders.

• Query, prepare, join, and validate structured datasets using SQL and Python-based data-processing libraries such as Pandas or Polars.
• Build reproducible data-preparation and experimentation workflows in an enterprise data or lakehouse environment.
• Explore data quality, availability, leakage, bias, outliers, and missing values before making modeling decisions.
• Produce clear visualizations and analytical summaries that make findings actionable.

• Package models for production use through containerized services and APIs.
• Work with engineering teams to integrate model outputs into broader operational and AI-agent-orchestrated workflows.
• Deploy and operate models using public-cloud data and ML services.
• Implement practical model monitoring for data quality, performance, drift, failures, and operational health.
• Support versioning, reproducibility, release workflows, retraining, troubleshooting, and continuous model improvement.

• Strong hands-on experience delivering Data Science or machine-learning solutions beyond the proof-of-concept stage.
• Good theoretical and practical command of fundamental Data Science and ML algorithms.
• Strong Python-based data analysis skills using Pandas or Polars, together with solid SQL.
• Practical experience with gradient boosting, clustering, time-series methods, prediction, and detection tasks.
• Experience formulating and solving optimization problems with real-world operational constraints.
• Ability to design sound experiments, validation strategies, back-testing approaches, and performance metrics.
• Experience working with enterprise data platforms, large datasets, and data-visualization tools.
• Production deployment experience covering containerization, API delivery, public-cloud environments, model monitoring, and MLOps.
• Ability to move from an ambiguous business problem to a working, testable, and maintainable solution.
• Clear communication skills and the ability to collaborate with data, engineering, product, and domain stakeholders.

• Core data work:
  Python, SQL, Pandas or Polars, exploratory analysis, data validation, and feature engineering.
• Modeling: gradient boosting, clustering, time series, prediction, detection, forecasting, and optimization.
• Data platforms: enterprise lakehouse and distributed data-processing environments.
• Experimentation: baselines, cross-validation, back-testing, error analysis, sensitivity analysis, and reproducibility.
• Deployment: public-cloud ML and data services, Docker/containerization, and API-based model delivery.
• MLOps: model versioning, monitoring, drift detection, retraining workflows, and operational diagnostics.
• Communication: data visualization, analytical reporting, and clear explanation of model behavior and trade-offs.

• Experience with aviation operations, aviation tools, or industry terminology.
• Experience with fuel-efficiency, operational planning, transport, routing, or resource-optimization problems.
• Practical experience with reinforcement learning where it is genuinely appropriate to the problem.

The role is full-time and onsite in Abu Dhabi, UAE. The selected candidate will be employed through the designated UAE company. Planned support includes the UAE employment visa, employee medical insurance, an initial flight to the UAE, a return flight at the end of the assignment, an approved broker fee, apartment-search assistance and local arrival support (subject to the final written offer).