Senior Data Scientist

Overview

Job Summary

Key Responsibilities

• Lead the development and deployment of generative AI and NLP solutions for engineering applications within the platform, including data exploration and analysis of large domain-specific datasets from both structured and unstructured sources to identify patterns and ensure data quality standards are met.
• Design, fine-tune, and deploy Large Language Models (LLMs) to interpret complex regulatory texts such as building codes and military standards, extracting structured rules for automated compliance checking and validation.
• Convert interpreted regulatory content into computer-processable formats (e.g., object-property-condition-value tuples) to enable execution by downstream compliance engines and systems.
• Architect NLP-driven methods to map natural language requirements directly to metadata entities across various schemas (e.g., linking 'systems design' to specific attributes in engineering models).
• Implement Retrieval-Augmented Generation (RAG) pipelines to enable high-accuracy querying of technical documentation and historical project data while minimizing hallucination risks in generated outputs.
• Develop time-series forecasting models to predict spend categories and material demand by integrating internal ERP data with external macroeconomic signals for supply chain optimization.
• Build machine learning classifiers to categorize supplier risks and operational anomalies, integrating diverse data sources to generate dynamic risk scores and operational insights.
• Design and oversee robust data extraction pipelines to transform raw data from Data Lakehouse environments, external web sources, SAP databases, and other systems into actionable features for predictive modeling and automated rule validation.
• Collaborate with backend engineers to integrate AI models into cohesive compliance and risk engines, ensuring seamless programmatic invocation via well-documented APIs.
• Optimize model performance to handle large-scale datasets and high-volume processing (e.g., analyzing thousands of supplier records) within operational timeframes, leveraging batching or asynchronous processing where necessary.
• Validate model outputs against test cases and historical data, debugging false positives/negatives to refine algorithms and ensure defense-grade reliability and accuracy in all applications.

Qualifications And Requirements

• 5+ years of experience in Data Science or Machine Learning, with a proven track record of deploying models into production environments.
• Expert proficiency in Python and standard ML libraries, including TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.
• Deep experience with transformer-based models (GPT, BERT, Llama) and prompt engineering techniques such as few-shot learning and fine-tuning for domain-specific tasks.
• Strong grasp of both supervised and unsupervised learning techniques.
• 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, including structured and unstructured sources.
• Understanding of how to expose models via RESTful APIs (Flask/FastAPI) and integrate them into larger software architectures.
• Solid understanding of statistics, probability distributions, and A/B testing, with the ability to identify and mitigate biases in datasets.
• Ability to quickly grasp complex domain terminology (e.g., construction regulations, defense standards, supply chain taxonomies) and translate them into logical workflows.
• Experience working in structured delivery models such as Agile or Sprint-based frameworks while adhering to rigorous validation and verification standards.
• Strong communication skills to collaborate effectively with Domain Experts, Backend Engineers, and Product Managers, aligning model outputs with real-world business logic.

Technical Skills

• Expert proficiency in Python and standard machine learning libraries, including TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy, with a strong grasp of both supervised and unsupervised learning techniques.
• Deep experience with transformer-based large language models (GPT, BERT, Llama) and advanced prompt engineering techniques, such as few-shot learning and fine-tuning, tailored for domain-specific applications.
• Proficiency in handling complex data structures (JSON, XML) and querying databases using SQL/NoSQL, with experience extracting data from specialized formats and graph data structures.
• Understanding of backend systems and the ability to expose AI models via RESTful APIs using frameworks like Flask or FastAPI, integrating them into larger software architectures.
• Solid foundation in statistics, probability distributions, and A/B testing, with expertise in identifying and mitigating biases in datasets.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines to enhance accuracy and reduce hallucination in technical documentation and historical project data retrieval.
• Familiarity with data extraction pipelines to transform raw data from sources such as Data Lakehouse, external web platforms, SAP databases, and other structured/unstructured repositories into actionable features for predictive modeling and automated rule validation.
• Knowledge of model orchestration and optimization techniques to ensure high-performance execution, including batching, asynchronous processing, and integration with compliance or risk engines via robust APIs.
• Ability to validate model outputs against test cases and historical data, debugging false positives/negatives to achieve defense-grade reliability and accuracy.

Company And Project Focus

Location and Work Environment

• Primary Location: Abu Dhabi, United Arab Emirates
• Company: Sister Company of [the client]
• Project Focus: The platform and Intelligent Supply Chain initiatives
• Work Setting: Hybrid or on-site collaboration with cross-functional teams, including Data Engineers, Backend Engineers, and Domain Experts.
• Delivery Model: Structured 'Sprint Zero' to 'Stage Gate' framework, ensuring rigorous validation and iterative deployment of AI-driven solutions.

Why Join This Role?