Data Scientist

Project Overview

Our client, an electric utility organization, is working to develop an AI/ML-based predictive safety solution for field crews involved in power restoration activities during outages and severe weather events.

Field crews operate in dynamic and potentially hazardous environments where weather conditions, terrain, equipment, fatigue, working conditions, and restoration activities can contribute to workplace safety incidents.

The objective is to develop a predictive model that can estimate the probability of a safety incident for a specific crew assignment by analyzing historical incident data along with operational, environmental, geographical, and crew-related information.

The solution will enable utility operators to identify high-risk assignments before crew deployment and take appropriate preventive measures, moving from reactive incident management toward proactive accident prevention.

Position Overview

We are seeking a hands-on Data Scientist with strong experience across Artificial Intelligence, Machine Learning, Computer Vision, Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI.

The ideal candidate should enjoy solving complex business problems, experimenting with emerging AI technologies, building proofs of concept, and transforming successful solutions into production-ready applications.

Strong technical skills are essential, along with excellent communication, collaboration, problem-solving abilities, and enthusiasm for working with emerging AI technologies.

Key Responsibilities

• Design, develop, evaluate, and deploy AI/ML models for real-world business use cases.
• Develop predictive models for risk assessment, safety prediction, classification, and anomaly detection.
• Work with structured and unstructured data, including operational, environmental, geographical, historical incident, crew, image, and other relevant datasets.
• Perform data exploration, preprocessing, feature engineering, statistical analysis, model development, and model evaluation.
• Develop Computer Vision solutions for image and video analysis, classification, object detection, recognition, and related use cases.
• Develop and experiment with Generative AI and LLM-based applications.
• Design and implement RAG solutions using enterprise and domain-specific data.
• Work with embeddings, vector search, vector databases, retrieval pipelines, prompt engineering, and context management.
• Build Agentic AI workflows involving multi-step reasoning, tool/API integration, orchestration, and automated decision-support processes.
• Evaluate and optimize AI/ML models for accuracy, reliability, scalability, and performance.
• Develop prototypes and Proofs of Concept (PoCs) and transition successful solutions toward production.
• Collaborate with business and technical stakeholders to understand requirements and translate business problems into effective AI/ML solutions.
• Present technical concepts, findings, model results, and recommendations to both technical and non-technical stakeholders.
• Stay current with emerging AI/ML, GenAI, LLM, Computer Vision, RAG, and Agentic AI technologies.
• Identify opportunities to apply modern AI technologies to improve operational safety and business outcomes.

Required Qualifications

• Strong hands-on experience in Data Science, Artificial Intelligence, and Machine Learning.
• Strong programming experience with Python.
• Experience with commonly used Data Science and ML libraries/frameworks such as Pandas, NumPy, Scikit-learn, Tensor Flow, PyTorch, or equivalent.
• Practical experience developing, training, evaluating, and improving machine learning models.
• Experience with predictive modeling, classification, regression, feature engineering, and model evaluation.
• Hands-on experience with Computer Vision technologies and frameworks.
• Experience with Generative AI and Large Language Models (LLMs).
• Hands-on experience designing or implementing RAG architectures.
• Strong understanding of embedding’s, vector search/vector databases, retrieval pipelines, prompt engineering, and context management.
• Understanding and practical experience with AI Agents / Agentic AI workflows.
• Experience with AI orchestration, multi-step workflows, and tool/API integration.
• Experience working with both structured and unstructured datasets.
• Strong analytical, problem-solving, and critical-thinking skills.
• Excellent communication and collaboration skills.
• Ability to explain complex technical concepts to both technical and non-technical stakeholders.
• Must be willing to relocate to Texas and work onsite from Day One.

Preferred Qualifications

• Experience taking AI/ML solutions from PoC/prototype through production.
• Experience with MLOps, model deployment, APIs, containers, CI/CD, and production ML pipelines.
• Experience with cloud-based AI/ML platforms and services such as AWS, Azure, or Google Cloud.
• Experience with modern LLM frameworks and AI development platforms.
• Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, Chroma, or equivalent.
• Experience with AI/LLM orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Experience with large-scale structured and unstructured datasets.
• Experience developing AI solutions for utilities, energy, field operations, safety, industrial, or other operational environments is a plus.
• Experience working with image/video data and multimodal AI is a plus.

Core Technical Skills

• Must Have:
• Data Science / AI / Machine Learning
• Python
• Machine Learning Modeling
• Computer Vision
• Generative AI / LLM
• RAG
• Embeddings & Vector Search
• Vector Databases
• Agentic AI / AI Agents
• AI Orchestration
• Structured & Unstructured Data
• Model Evaluation & Optimization
• Good to Have:
• MLOps
• Cloud AI/ML
• Model Deployment
• APIs & Containers
• LangChain / LangGraph / LlamaIndex
• TensorFlow / PyTorch
• Multimodal AI
• Utility / Energy / Field Operations experience