Data Scientist
- Hiring from
- United States
- Work type
- Hybrid
- Posted
617,703 remote jobs, straight from company career pages
100% free · New jobs every minute
Show job descriptionHide job description
Job Description
Job Title: Data
Scientist
Location: Wayne,
IN 46809 (Hybrid)
Duration:
6-12 months contract
Position
Summary:
· We are seeking a highly skilled and motivated
Data Scientist with a strong background in Large Language Models (LLMs) and
Retrieval-Augmented Generation (RAG) systems.
· The ideal candidate has direct experience
applying AI within the utility, energy, or industrial sector.
· You will lead the design, development, and
deployment of intelligent systems that translate unstructured operational data
(technical manuals, maintenance logs, field reports) into actionable insights.
Key
Responsibilities:
· Develop RAG Pipelines: Design and
optimize Retrieval-Augmented Generation (RAG) models to enhance LLM-driven
troubleshooting and field operations.
· AI Model Development: Fine-tune and
evaluate LLMs (e.g., GPT-4, Llama 3) for task-specific reasoning and accuracy.
· Knowledge Integration: Build high-quality
Python code to integrate vector databases (e.g., Pinecone, Milvus, Weaviate) to
store and retrieve technical utility documents efficiently.
· Operational Intelligence: Apply LLM
solutions to analyze unstructured utility data, such as asset management logs,
SCADA alerts, and maintenance records.
· Deployment: Work with software engineers
to deploy and scale models in cloud environments (Azure/AWS).
· Collaboration: Work cross-functionally
with field operations, engineering teams, and stakeholders to understand data
needs and deliver solutions.
Required
Qualifications:
· Experience: Professional experience in
Data Science/Machine Learning, with at least 1 year focusing specifically on
LLMs and Generative AI.
· Technical Skills: Advanced proficiency in
Python (PyTorch/TensorFlow, LangChain, LlamaIndex) and SQL.
· Domain Knowledge: Proven experience in
the utility, energy, oil & gas, or heavy industrial sectors.
· RAG Expertise: Hands-on experience with
vector embeddings, semantic search, and prompt engineering.
