Data Scientist - USA | Remote

TRENT HOEK OUTDOORS LLC
United States
10 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$140,000
Working hours
Regular working hours
Job source

Tech stack

Data Cleansing Machine Learning Natural Language Processing Software Deployment Feature Engineering Chatbots Large Language Models Multi-Agent Systems Model Validation Document Classification

Job description

3 Dots IT Solutions is seeking a Data Scientist specializing in Conversational AI and Natural Language Processing (NLP) for an investment partner. This is a remote position open to candidates located within the United States. Candidates should be available for a face-to-face interview as part of the selection process., * Build and enhance complex virtual assistants, including conversation design, intent and entity modeling, error analysis, and production improvements.

  • Develop and improve NLP and machine learning solutions, including text classification, embeddings, and models based on Transformer architecture and large language models (LLMs).
  • Perform machine learning tasks such as feature engineering, time-series forecasting, data preprocessing, and model evaluation.
  • Work with agentic AI and multi-agent architectures, including evaluation frameworks, golden datasets, and LLM-as-a-Judge approaches.
  • Support model fine-tuning and continuous improvement of AI systems.
  • Improve the performance of AI models in production through inference optimization, KV caching, latency reduction, scalability, and deployment practices.

Requirements

  • Strong expertise in Natural Language Processing (NLP), including text classification and embeddings.
  • Solid understanding of LLM fundamentals and Transformer architecture.
  • Hands-on experience with machine learning modeling, feature engineering, time-series forecasting, data preprocessing, and model evaluation.
  • Experience building and enhancing conversational AI systems, including virtual assistants, intent and entity modeling, and conversation design.
  • Knowledge of multi-agent architectures and agentic AI evaluation methods.
  • Understanding of production deployment and optimization of machine learning and LLM systems, including inference performance, KV caching, latency, and scalability.
  • Ability to attend a face-to-face interview during the selection process.

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