Senior Data Scientist (UAE)

This is a remote position.

• Location: Remote – UAE
  
• Requirement: A Valid UAE work permit/employment visa is mandatory.
  
• Employment type: Independent Contractor
  

• Data Exploration and Analysis: Query and analyse large domain- or topic-specific data sets from both structured and unstructured sources, identify patterns and features. Ensure data meets quality standards and requirements before model development.
  
• Regulation Text Interpretation: Design and fine-tune Large Language Models (LLMs) to parse complex regulatory texts (e.g., building codes, military standards) and extract structured rules for automated compliance checking.
  
• Rule Formalization: Convert interpreted regulations into computer-processable formats (e.g., object-property-condition-value tuples) that can be executed by downstream compliance engines.
  
• Querying via NLP: Architect methods for LLMs to map natural language requirements directly to specific metadata entities within various schemas (e.g., mapping "systems design" to specified attributes).
  
• RAG Architecture: Implement Retrieval-Augmented Generation (RAG) pipelines that allow systems to query vast repositories of technical documentation and historical project data with high accuracy and low hallucination rates.
  

• Forecasting Engines: Develop time-series forecasting models to predict spend categories and material demand by correlating internal ERP data with external macroeconomic signals.
  
• Classification & Risk Scoring: Build machine learning classifiers to categorize supplier risks and operational anomalies, integrating data from diverse sources to create dynamic risk scores.
  
• Data Extraction Pipelines: Design robust pipelines to extract and transform raw data (from Data Lakehouse, external web sources, or SAP and other databases) into features required for predictive modeling and automated rule checking.
  

• Model Orchestration: Work with Back End Engineers to integrate AI models into a cohesive "compliance engine" or "risk engine" that can be invoked programmatically via robust APIs.
  
• Optimization: Streamline model performance to ensure complex checks (e.g., analyzing large datasets or processing thousands of supplier records) can be executed within reasonable timeframes, potentially using batching or asynchronous processing.
  
• Quality Assurance: Validate model outputs against known test cases and historical data, debugging false positives/negatives to refine algorithms and ensure "defense-grade" reliability.
  

• Core AI/ML: Expert proficiency in Python and standard ML libraries (TensorFlow/PyTorch, Scikit-learn, Pandas, NumPy). Strong grasp of both supervised and unsupervised learning techniques.
  
• NLP & LLMs: Deep experience with transformer-based models (GPT, BERT, Llama) and prompt engineering techniques (few-shot learning, fine-tuning) for domain-specific tasks.
  
• Data Engineering: Proficiency in handling complex data structures (JSON, XML) and familiarity with database querying (SQL/NoSQL) or graph data structures. Experience with data extraction from specialized formats is a significant plus.
  
• Backend Awareness: Understanding of how to expose models via RESTful APIs (Flask/FastAPI) and integrate them into larger software architectures.
  
• Statistics: Solid understanding of statistics, probability distribution, A/B testing. Adept at identifying and mitigating biases in datasets