Numentica

Senior Data Scientist / Machine Learning Engineer (NLP)

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Scientist/Machine Learning Engineer (NLP) in Calabasas, CA or Las Vegas, NV, offering a 6-month contract. Requires 4-6+ years of experience in Data Science, production-grade NLP models, and proficiency in Python, PySpark, and SQL.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
Unknown
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πŸ—“οΈ - Date
July 17, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
On-site
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
Calabasas, CA
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🧠 - Skills detailed
#ML (Machine Learning) #Model Evaluation #NLP (Natural Language Processing) #Spark (Apache Spark) #Monitoring #Data Pipeline #Classification #Data Science #PySpark #Datasets #Python #Pandas #Trend Analysis #Spark SQL #MLflow #SQL (Structured Query Language) #Data Processing #Databricks #Anomaly Detection #Scala
Role description
Job Title: Senior Data Scientist / Machine Learning Engineer (NLP) Location: Calabasas, CA / Las Vegas, NV (Onsite – 4 Days/Week) Duration: 6 Months Contract Work Authorization: USC, GC, and all valid EADs (No H1B, OPT, CPT) Must-Have Skills β€’ 4–6+ years of Data Science / Machine Learning experience β€’ Production-grade NLP Classification Models β€’ Intent, Topic, Sentiment & Multi-label Classification β€’ Python, PySpark, SQL, Pandas β€’ Customer message/call transcript analytics β€’ Trend & Anomaly Detection β€’ Dataset labeling & annotation workflows β€’ Model evaluation (Precision, Recall, F1, Confusion Matrix) β€’ Model monitoring & Drift Detection β€’ PII-safe data processing Responsibilities β€’ Build and deploy NLP models for customer communications. β€’ Develop intent, topic, and sentiment classification systems. β€’ Design scalable Python/PySpark data pipelines. β€’ Prepare and clean transcript/message datasets. β€’ Build anomaly detection and trend analysis models. β€’ Create high-quality labeled datasets and annotation guidelines. β€’ Monitor model performance, confidence scores, and drift. β€’ Ensure secure processing of sensitive customer data. Nice to Have β€’ Databricks β€’ MLflow β€’ Unity Catalog β€’ Databricks Workflows β€’ Model/Data Versioning β€’ LLM-assisted Classification β€’ Retrieval & Embedding Models β€’ Contact Center Analytics β€’ Property Management / Real Estate domain