Overview
On Site
Contract - W2
Skills
Statistics
Modeling
FOCUS
Machine Learning (ML)
Testing
Collaboration
Analytical Skill
Statistical Models
Clustering
Probability
Data Analysis
Distribution
Natural Language Processing
Named-Entity Recognition (NER)
Ontologies
Extraction
Evaluation
Machine Learning Operations (ML Ops)
Python
Data Science
Communication
Artificial Intelligence
Job Details
Title: AI Data Scientist Contractor
Location: Charlotte, NC or Dallas, TX (100% Onsite)
Duration: Contract - 6 month with potential extension
Must be located in Dallas or Charlotte - role is on-site 5 days/week
What You'll Get to Do:
Location: Charlotte, NC or Dallas, TX (100% Onsite)
Duration: Contract - 6 month with potential extension
Must be located in Dallas or Charlotte - role is on-site 5 days/week
What You'll Get to Do:
- Perform statistical analysis, clustering, and probability modeling to drive insights and inform AI-driven solutions
- Analyze graph-structured data to detect anomalies, extract probabilistic patterns, and support graph-based intelligence
- Build NLP pipelines with a focus on NER, entity resolution, ontology extraction, and scoring
- Contribute to AI/ML engineering efforts by developing, testing, and deploying data-driven models and services
- Apply ML Ops fundamentals, including experiment tracking, metric monitoring, and reproducibility practices
- Collaborate with cross-functional teams to translate analytical findings into production-grade capabilities
- Prototype quickly, iterate efficiently, and help evolve data science best practices across the team
- Solid experience in statistical modeling, clustering techniques, and probability-based analysis
- Hands-on expertise in graph data analysis, including anomaly detection and distribution pattern extraction
- Strong NLP skills with practical experience in NER, entity/ontology extraction, and related evaluation methods
- An engineering-forward mindset with the ability to build, deploy, and optimize real-world solutions (not purely theoretical)
- Working knowledge of ML Ops basics, including experiment tracking and key model metrics
- Proficiency in Python and common data science/AI libraries
- Strong communication skills and the ability to work collaboratively in fast-paced, applied AI environments
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