Senior Data Scientist

Contract duration: 6 months (with a potential extension) Engagement Type: Full-time Start date: July 2026 Location: Abu Dhabi (on-site) Role Overview 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. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact. Key Responsibilities 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 (reliability, security, scalability). Data Engineering & Feature Systems • Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability. • Build and manage feature pipelines and feature stores, ensuring 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 (Python/JS), 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, batch jobs) using modern frameworks. • Balance model sophistication with system performance, latency, and operational constraints. MLOps, DevOps & 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 both 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 (latency, throughput, cost). • Apply secure coding practices, access controls, and data protection standards. • Manage technical debt and ensure long-term maintainability of production systems. Documentation, Standards & Engineering Excellence • Produce developer-focused documentation (APIs, architecture diagrams, 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 across the consultancy. Required Experience and Qualifications • Prior experience at management consulting firms and/or Big Tech is an advantage. • Client-serving experience is an advantage. • 5+ years of experience in data science or a related analytical field. • 5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production. • Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security. • Prior experience in developing AI solutions on public cloud services is an advantage. • Bachelor s degree (Master s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering). • Technical Expertise: Coding & data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git. • Statistics & experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design. • ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised). • Data prep & analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals. • Prior experience at management consulting firms and/or Big Tech is an advantage. • Client-serving experience is an advantage. • 5+ years of experience in data science or a related analytical field. • 5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production. • Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security. • Prior experience in developing AI solutions on public cloud services is an advantage. • Bachelor s degree (Master s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering). • Technical Expertise: Coding & data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git. • Statistics & experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design. • ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised). • Data prep & analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.