Data Scientist

SilentEight

Responsibilities

• Lead the data science lifecycle, from discovery and exploration to delivery and customer hand-off.

• Translate ambiguous business problems into clear problem statements and deliver working, production-ready solutions.

• Partner with customers and internal stakeholders to co-design use cases, define success criteria, and ensure models are adopted and successful in production.

• Build quick, iterative prototypes to test ideas, and work cross-functionally to deploy validated solutions into production environments.

• Own and improve pipelines for data integration, validation, and monitoring, ensuring long-term stability and performance.

• Analyze large-scale structured and unstructured datasets to extract insights and power model development.

• Apply machine learning, NLP, graph analytics, and other advanced techniques to solve challenging problems such as semantic search, or anomaly detection.

• Create and maintain internal data science tooling to streamline experimentation, testing, and delivery.

• Support internal teams and customers with issue resolution, ensuring data science solutions continue to deliver value post-deployment.

• Follow and advocate for best practices in MLOps, DataOps, and clean code to ensure reproducibility, maintainability, and scalability.

Requirements

• Based in the United Arab Emirates (remote-first team; flexible working environment).

• Bachelor s or Master s degree in Data Science, Computer Science, Statistics, or a related field.

• 3-5 years of demonstrated experience with LLM technologies (LangChain, LlamaIndex, OpenAI APIs, etc.) and building NLP applications.

• Deep proficiency in Python: you write clean, modular, maintainable, and testable code suitable for real-world deployments.

• Solid SQL skills and hands-on experience with large-scale data (e.g., PySpark, BigQuery).

• Skilled in graph analysis, machine learning, and unstructured data workflows.

• Demonstrated ability to apply software engineering best practices (e.g., modular design, clean code, unit testing, CI/CD) in data science and machine learning projects, ensuring production-readiness and maintainability.

• Ability to drive end-to-end initiatives, from exploration to delivery, where solutions must be state-of-the-art, business-aligned, and regulatory-compliant.

• Comfortable working in Linux environments and using collaborative tools (e.g., Git, Docker).

• Strong communication skills translate technical complexity into actionable business narratives, driving clarity, alignment, and decision-making across all levels of the organization.

• Mindset of ownership: you re excited to see projects through, from first prototype to deployed, customer-facing solution.