Applied AI Data Scientist

Job Information

• Permanent W-2 Employee / Corp2Corp Contractor | Omm IT Solutions | United States
• Posted On 03/24/2026
• Start Date 03/26/2026 12:00 AM
• Compensation Negotiable
• IT Services
• Work Authorization USC/GC/H1B
• Job Opening ID Omm2856J
• State/Province Texas
• City Dallas
• 75201

Job Description

PLEASE NOTE:

• It is a 100% Onsite position in Dallas, TX.
• Local Candidates preferred.

KEY REQUIRED SKILLS:

• Involves statistical analysis, NLP, graph data, ML Ops, and developing/deploying AI solutions.
• Eligible for Hacker Rank assessment.

JOB DESCRIPTION:

• 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

REQUIRED SKILLS:

• 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.