AI Data Scientist
About The Position
For one of our ongoing multiyear opportunity based out of Dallas, TX, we are looking for a AI engineer. 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 The consultant should have 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 .
Requirements
- 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
Responsibilities
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
