Applied AI Data Scientist

Job Title: Applied AI Data Scientist
Location: Dallas, TX or Charlotte, NC
Work Mode: Onsite 5 Days/Week
Rate: $65-70/hr
Employment Type: Contract

Role Overview

We are seeking an experienced Applied AI Data Scientist to join a fast-paced team focused on building and deploying AI-driven solutions. This role requires strong expertise in statistical modeling, graph data analysis, and natural language processing (NLP), along with the ability to translate analytical insights into production-ready AI/ML capabilities.

The ideal candidate will combine strong data science fundamentals with an engineering mindset to design, develop, and deploy scalable machine learning models and pipelines in a collaborative environment.

Key Responsibilities

• Perform statistical analysis, clustering, and probability modeling to generate insights and support AI-driven solutions.

• Analyze graph-structured data to detect anomalies and extract probabilistic patterns for graph-based intelligence.

• Build and maintain NLP pipelines focusing on Named Entity Recognition (NER), entity resolution, ontology extraction, and scoring models.

• Develop, test, and deploy AI/ML models and data-driven services in production environments.

• Apply MLOps practices, including experiment tracking, model monitoring, and reproducibility standards.

• Collaborate with cross-functional teams to translate analytical findings into scalable and production-grade capabilities.

• Rapidly prototype solutions, iterate on models, and contribute to improving data science best practices across the team.

Required Qualifications

• Strong experience in statistical modeling, clustering techniques, and probability-based analysis.

• Hands-on experience with graph data analysis, including anomaly detection and pattern extraction.

• Strong NLP expertise, including Named Entity Recognition (NER), entity resolution, and ontology extraction.

• Engineering-oriented mindset with the ability to build, deploy, and optimize real-world AI/ML solutions.

• Working knowledge of MLOps fundamentals, including experiment tracking and model performance metrics.

• Proficiency in Python and common AI/ML and data science libraries.

• Strong communication and collaboration skills.

• Ability to work effectively in fast-paced, applied AI environments.