Senior Data Scientist

Contract duration: 6 months, with a potential extension.
Engagement type:
Full‑time.

Start date:

July 2026.

Location:

Abu Dhabi (on‑site).

As a Senior Data Scientist, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end‑to‑end Data & AI solutions, translating complex business challenges into scalable, production‑ready analytics and machine learning systems. You will collaborate closely with product, data engineering, and architecture stakeholders to deliver measurable impact.

• Use Case Framing & Solution Design
  • Translate client business problems into end‑to‑end system architectures that combine Data, ML, and software components.
  • Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.
  • Make explicit trade‑offs across performance, cost, latency, and maintainability.
  • Define success metrics, SLAs, and non‑functional requirements.
• Data Engineering & Feature Systems
  • Design and implement robust data pipelines—batch and streaming—ensuring quality, lineage, and observability.
  • Build and manage feature pipelines and feature stores, maintaining consistency between training and inference.
  • Collaborate with platform teams to define data models, schemas, and storage strategies.
  • Enforce standards for data validation, testing, and monitoring within production systems.
• Applied ML & Production‑Grade Development
  • Develop ML solutions using production‑quality code in Python or JavaScript, following software engineering best practices.
  • Structure codebases into maintainable, testable modules with clear separation of concerns.
  • Implement unit, integration, and end‑to‑end tests for data and ML components.
  • Package models and logic into deployable services—APIs, microservices, or batch jobs—using modern frameworks.
  • Balance model sophistication with system performance, latency, and operational constraints.
• MLOps, Dev Ops & Platform Integration
  • Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.
  • Containerize and deploy services using Docker, Kubernetes, and cloud‑native tooling.
  • Implement model versioning, experiment tracking, and artifact management.
  • Design monitoring and observability systems—logs, metrics, alerts—for data and model performance.
  • Automate retraining, rollback, and release strategies to ensure system resilience.
• System Reliability, Scalability & Security
  • Design systems for high availability, fault tolerance, and horizontal scalability.
  • Optimize performance across data pipelines and inference services for latency, throughput, and cost.
  • Apply secure coding practices, access controls, and data protection standards.
  • Manage technical debt and ensure long‑term maintainability of production systems.
• Documentation & Engineering Excellence
  • Produce developer‑focused documentation, APIs, architecture diagrams, and runbooks.
  • Establish and enforce coding standards, review processes, and engineering best practices.
  • Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.
  • Drive continuous improvement in engineering maturity, tooling, and delivery practices.

• 5+ years of experience in data science or a related analytical field, delivering end‑to‑end analytics/ML solutions from problem framing through deployment and ongoing monitoring.
• Experience applying software engineering methodologies—coding standards, code reviews, build processes, testing, and security.
• Prior experience developing AI solutions on public cloud services is an advantage.
• Bachelor's degree (Master's preferred) in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or Engineering.
• Technical expertise: coding and data querying in Python (pandas/Num Py) and SQL;
  Git proficiency.
• Statistical and experimental skills: probability, hypothesis testing, regression, A/B testing, and experimental design.
• Machine learning: feature engineering, model selection, cross‑validation, metrics, hyperparameter tuning; supervised and unsupervised methods.
• Data preparation and analysis: ETL, EDA, data cleaning, handling missing values/outliers, and building insight narratives with visuals.
• Preferred: prior experience at management consulting firms or Big Tech, and client‑serving experience.