

Suncap Technology
Senior AI Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior AI Data Scientist contractor based in Charlotte, NC or Dallas, TX, for a 6-month, onsite position. Key skills include statistical modeling, graph data analysis, NLP, and proficiency in Python.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
December 3, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Dallas, TX
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🧠 - Skills detailed
#NLP (Natural Language Processing) #Data Analysis #HBase #ML Ops (Machine Learning Operations) #AI (Artificial Intelligence) #"ETL (Extract #Transform #Load)" #Data Science #Python #Monitoring #NER (Named-Entity Recognition) #Anomaly Detection #Clustering #ML (Machine Learning) #Libraries
Role description
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:
• 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
What You'll Bring with You:
• 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
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:
• 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
What You'll Bring with You:
• 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






