Data Scientist

Role Summary

Senior Data Scientist

LeverEDGE

Intelligent Supply Chain

Key Responsibilities

• **Generative AI & NLP for Engineering (LeverEDGE)**
• **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.
• **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.
• Liaise with Data Engineers on
• **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.

Technical 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.

Professional Qualifications

• **Experience:**
• 5+ years of experience in Data Science or Machine Learning, with a proven track record of deploying models into production environments.
• **Domain Adaptability:**
• Ability to quickly grasp complex domain terminology (e.g., construction regulations, defense standards, supply chain taxonomies) and translate them into logic.
• **Structured Delivery:**

Experience

• working in structured delivery models (Agile/Sprint-based) while adhering to rigorous validation and verification standards.
• **Collaboration:**
• Strong communication skills to work effectively with Domain Experts, Backend Engineers, and Product Managers to align model outputs with real-world business logic.
• Why This Role?
• This is not just about building models; it is about building the intelligence that drives the industrial backbone of the future.
• You will be solving tangible, high-stakes problems —from ensuring product design and manufacturing being safe and compliant -, to predicting supply chain disruptions before they impact national security capabilities.
• Join us to turn data into decision advantage.