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Senior Data Scientist
cander
Abu Dhabi, UAE
Full Time
Senior
Hybrid
6 days ago
PythonTensorFlowPyTorchScikit learnPandasNumPy
Free
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About the Role
A defense-focused AI company in Abu Dhabi seeks a Senior Data Scientist to lead development and deployment of generative AI and NLP solutions for engineering and supply chain applications.
Key Skills for This Role
PythonTensorFlowPyTorchScikit learnPandasNumPy
Responsibilities
- Lead the development and deployment of generative AI and NLP solutions for engineering applications within the platform.
- Design, fine tune, and deploy Large Language Models (LLMs) to interpret complex regulatory texts such as building codes and military standards.
- Convert interpreted regulatory content into computer processable formats (e.g., object property condition value tuples).
- Architect NLP driven methods to map natural language requirements directly to metadata entities across various schemas.
- Implement Retrieval Augmented Generation (RAG) pipelines to enable high accuracy querying of technical documentation.
- Develop time series forecasting models to predict spend categories and material demand.
- Build machine learning classifiers to categorize supplier risks and operational anomalies.
- Design and oversee robust data extraction pipelines to transform raw data from various sources into actionable features.
- Collaborate with backend engineers to integrate AI models into cohesive compliance and risk engines.
- Optimize model performance to handle large scale datasets and high volume processing within operational timeframes.
- Validate model outputs against test cases and historical data, debugging false positives/negatives to ensure defense grade reliability.
Requirements
- 5+ years of experience in Data Science or Machine Learning with a proven track record of deploying models into production
- Expert proficiency in Python and standard ML libraries (TensorFlow, PyTorch, Scikit learn, Pandas, NumPy)
- Deep experience with transformer based models (GPT, BERT, Llama) and prompt engineering techniques
- Strong grasp of both supervised and unsupervised learning techniques
- Proficiency in handling complex data structures (JSON, XML) and database querying (SQL/NoSQL)
- Experience with data extraction from specialized formats
- Understanding of how to expose models via RESTful APIs (Flask/FastAPI)
- Solid understanding of statistics, probability distributions, and A/B testing
- Ability to quickly grasp complex domain terminology
- Experience working in structured delivery models such as Agile or Sprint based frameworks
- Strong communication skills to collaborate effectively with Domain Experts, Backend Engineers, and Product Managers
Full Job Posting
Job Summary
- We are seeking a Senior Data Scientist to lead the technical development and deployment of high impact AI initiatives for advanced defense capabilities.
- You will transition from exploratory modeling to building production grade AI systems that directly enhance design, manufacturing, and procurement processes.
- You will serve as the technical liaison between unstructured data sources and structured engineering systems, including BIM/IFC models and SAP S/4HANA.
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.
- 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.
- Convert interpreted regulatory content into computer processable formats (e.g., object property condition value tuples) to enable execution by downstream compliance engines.
- Architect NLP driven methods to map natural language requirements directly to metadata entities across various schemas.
- Implement Retrieval Augmented Generation (RAG) pipelines to enable high accuracy querying of technical documentation and historical project data while minimizing hallucination risks.
- Develop time series forecasting models to predict spend categories and material demand by integrating internal ERP data with external macroeconomic signals.
- Build machine learning classifiers to categorize supplier risks and operational anomalies, integrating diverse data sources to generate dynamic risk scores.
- 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.
- 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 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.
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.
- 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
- Join a dynamic organization where innovation and data driven decision making are at the core of our mission.
- In this role, you will contribute to high impact projects focused on leveraging advanced analytics, machine learning, and statistical modeling to solve complex business challenges.
- The team operates at the intersection of technology and strategy, delivering scalable solutions that enhance operational efficiency, optimize performance, and drive growth.
- Your work will directly support initiatives aimed at transforming raw data into actionable insights, ensuring alignment with both short term objectives and long term organizational goals.
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?
- This role is more than just model development—it is about shaping the intelligent systems that will define the industrial foundation of tomorrow.
- You will tackle real world, high impact challenges, from safeguarding product design and manufacturing against safety and compliance risks to anticipating supply chain disruptions that could threaten national security.
- By joining our team, you will transform raw data into actionable insights, giving organizations a decisive edge in decision making and strategic foresight.
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