Applied AI Data Scientist - Contractor

Omm IT Solutions • Charlotte, NC

2d • Onsite

About The Position

Perform statistical analysis, clustering, and probability modelling 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.

Requirements

• Solid experience in statistical modelling, 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.

Responsibilities

• Perform statistical analysis, clustering, and probability modelling 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.

What This Job Offers

• Career Level: Mid Level
• Education Level: No Education Listed