Excelon Solutions

Data Scientist with GEN AI

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with GEN AI in Charlotte, NC, offering a hybrid work arrangement. The contract length is unspecified, with a pay rate of "unknown." Key skills include Generative AI, Machine Learning, and data analysis.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
May 22, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
Charlotte, NC
-
🧠 - Skills detailed
#Reinforcement Learning #Deployment #Datasets #"ETL (Extract #Transform #Load)" #AI (Artificial Intelligence) #ML (Machine Learning) #Transformers #Data Science #Data Analysis #Monitoring #Scala
Role description
Data Scientist with GEN AI Charlotte, NC (Local ONLY) Remote work Need F2F is required Responsibilities: • Develop and Implement Generative AI Models: Design, implement, and deploy state-of-the-art generative AI models (e. g., GANs, VAEs, Transformers, etc. ) to solve real-world problems. • Data Analysis and Preprocessing: Collect, clean, and preprocess large datasets to train AI models effectively. • Model Training & Evaluation: Train and fine-tune machine learning models, including supervised, unsupervised, and reinforcement learning algorithms, focusing on generative techniques. • Collaboration: Work closely with cross-functional teams, including software engineers, product managers, and business stakeholders, to identify opportunities and deliver AI-powered solutions. • AI Research: Stay up-to-date with the latest research in Generative AI and contribute to the research community through publications or internal knowledge-sharing. • Optimize and Scale AI Models: Enhance the scalability, performance, and reliability of generative AI systems deployed in production. • Experimentation: Conduct rigorous experimentation to test and validate the performance of AI models, with a focus on improving model quality and accuracy. • Tool Development: Develop tools for automating workflows around model training, validation, deployment, and monitoring. • Ethics and Fairness: Ensure that AI models adhere to ethical guidelines and are built with fairness and transparency in mind.