Senior Data Scientist

Job Description

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 archite