Staff Data Scientist
Staff Data Scientist
Location: Abu Dhabi, UAE
Company: Confidential Technology Organization
Project Focus: Advanced AI Systems & Intelligent Supply Chain
Role Summary
We are seeking a Staff Data Scientist to lead the technical execution of high-impact AI initiatives within a large-scale enterprise transformation programme. In this role, you will move beyond experimental modelling to build production-grade systems that support complex engineering, manufacturing, and procurement processes.
You will act as the technical bridge between unstructured data sources (regulatory text, technical documentation) and structured enterprise systems (BIM/IFC models, ERP platforms). Working within a structured delivery framework, you will design and deploy AI solutions supporting two flagship initiatives:
- Advanced Engineering AI
- Intelligent Supply Chain Analytics (predictive spend, forecasting, and risk analytics)
Key Responsibilities
- Generative AI & NLP for Engineering AI Programs
- Data Exploration & Analysis: Query and analyse large domain-specific datasets from structured and unstructured sources. Identify patterns, features, and data quality requirements prior to model development.
- Regulatory Text Interpretation: Design and fine-tune Large Language Models (LLMs) to parse complex regulatory and technical documentation and extract structured rules for automated compliance and validation.
- Rule Formalisation: Convert interpreted regulations into machine-readable formats (such as object-property-condition-value structures) to support automated compliance workflows.
- Natural Language Querying: Develop methods enabling LLMs to map natural language requirements directly to metadata entities across different schemas.
- RAG Architecture: Design and implement Retrieval-Augmented Generation (RAG) pipelines capable of querying large repositories of technical documentation and historical project data while minimising hallucinations and maximising accuracy.
- Predictive Modelling & Supply Chain Analytics
- Forecasting Engines: Develop time-series forecasting models to predict spend categories and material demand by combining internal ERP data with external economic indicators.
- Classification & Risk Scoring: Build machine learning models to classify supplier risks, operational anomalies, and other business-critical events.
- Data Pipelines: Design robust extraction and transformation pipelines to convert raw data from data platforms, enterprise applications, and external datasets into model-ready features.
- System Integration & Performance
- Model Orchestration: Collaborate with software engineering teams to integrate AI models into scalable compliance, recommendation, or risk-management platforms accessible through APIs.
- Performance Optimisation: Ensure AI systems operate efficiently across large datasets through optimisation techniques such as batching, parallelisation, and scalable processing methods.
- Quality Assurance: Validate model performance against known benchmarks and historical datasets, reducing false positives and false negatives while maintaining enterprise-grade reliability.
Technical Requirements
- Core AI / ML: Expert-level Python skills and experience with TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy. Strong understanding of supervised and unsupervised learning techniques.
- NLP & LLMs: Deep experience with transformer-based models (GPT, BERT, Llama, or equivalent), prompt engineering, fine-tuning, and domain adaptation.
- Data Engineering: Experience working with JSON, XML, SQL, NoSQL databases, and complex data structures. Familiarity with graph-based data models is advantageous.
- Backend Integration: Understanding of model deployment methodologies and exposure to RESTful API development using Flask, FastAPI, or similar frameworks.
- Statistics: Strong grounding in statistics, probability, experimentation, model validation, and bias mitigation.
Professional Qualifications
- Experience: 5+ years of experience in Data Science, Machine Learning, or Applied AI, with a track record of deploying models into production environments.
- Domain Adaptability: Ability to rapidly understand complex technical domains and translate business requirements into AI-powered solutions.
- Structured Delivery: Experience working within Agile, Sprint-based, or structured project delivery environments.
- Collaboration: Strong communication and stakeholder management skills, with the ability to work effectively alongside technical, operational, and business teams.
Why This Role?
This opportunity provides the chance to build AI solutions that solve complex real-world challenges at enterprise scale. You will play a key role in developing intelligent systems that improve engineering processes, automate decision-making, enhance operational efficiency, and strengthen supply chain resilience. Join a team focused on transforming data into measurable business outcomes.
GCS is acting as an Employment Business in relation to this vacancy.
Staff Data Scientist
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