Staff Data Scientist

Staff Data Scientist

Location: Abu Dhabi, UAE
Company: Confidential Technology Organization
Project Focus: Advanced AI Systems & Intelligent Supply Chain

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)

• 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.

• 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.

• 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 a