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 **Key Responsibilities** ***1\. Generative AI \& NLP for Engineering*** * **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. ***2\. Predictive Modeling \& Optimization (Supply Chain)*** * **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. ***3\. System Integration \& Performance*** * **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. ### **Requirements** * **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