Data Scientist

We are on the lookout for talented Data Scientists with a background in Software Engineering or DevOps, who are passionate about consulting clients on their business transformations.

This is a full-time, permanent role based in Dubai, UAE with frequent travels across the country.

As a Data Scientist or Senior Data Scientist, you will operate at the intersection of quantitative modeling, scalable architecture, and strategic problem-solving, embedded within a fast-scaling AI consultancy environment.

Your role will involve architecting advanced data science pipelines that leverage structured and unstructured data to drive algorithmic solutions with measurable impact across industries ranging from decarbonization strategies to next-gen supply chain intelligence.

You will work shoulder-to-shoulder with clients across sectors and geographies, designing domain-specific AI strategies and embedding ML systems into enterprise ecosystems. Projects span from low-latency inference engines and probabilistic decision systems to reinforcement learning in dynamic business contexts. Your contributions won't just end at model development—you'll also participate in productionizing solutions via container orchestration, CI/CD for ML, and integration into client-facing platforms.

Additionally, you will have the opportunity to contribute to intellectual capital through research publications, proprietary algorithm development, and presenting at technical conferences, representing the frontier capabilities of our client company.

We're looking for Data Scientists who:

• Are comfortable engaging directly with stakeholders to scope business problems and co-design machine learning solutions
• Possess the ability to abstract complex mathematical models into intuitive, visual artifacts for non-technical audiences
• Can communicate high-dimensional insights and statistical trade-offs with clarity and precision
• Are builders at heart—comfortable writing clean, testable code in languages such as Python or Scala, and familiar with containerized workflows (Docker, Kubernetes)
• Have led or contributed to large-scale projects involving supervised/unsupervised learning, time-series analysis, or simulation modeling
• Are well-versed in the theoretical underpinnings of ML algorithms (e.g., kernel methods, variational inference, stochastic optimization) and understand when and how to apply them
• Have practical experience implementing MLOps principles for reproducible experimentation and scalable deployment
• Bring strong organizational and project leadership skills, particularly in fast-moving, cross-functional environments