Senior Data Scientist - Only GC/USC on W2

5 days onsite at Las Vegas, NV, Monday-Wednesday at Calabasas, CA

Work Authorization: GC, USC

MUST be on W2, No Corp2Corp

Must Have:

• 4–6+ years of data science or machine learning experience
• NLP classification for customer messages or call transcripts
• Intent, topic, sentiment, and multi-label classification
• Confidence scoring and model evaluation
• Text cleaning, deduplication, speaker handling, and PII-safe processing
• Trend and anomaly detection
• Python, PySpark, SQL, and pandas
• Labeled dataset design and annotation workflows
• Precision, recall, confusion matrix, and drift monitoring

Responsibilities:

• Build and deploy NLP classification models for customer communications.
• Develop intent, topic, sentiment, and multi-label taxonomies.
• Clean and prepare transcript and message data for modeling.
• Handle short-text cases, duplicate records, system messages, and speaker identification.
• Build trend and anomaly detection methods using baselines, seasonality, and channel mix.
• Design maintainable Python and PySpark data pipelines.
• Define sampling strategies and annotation guidelines for labeled datasets.
• Support reviewer adjudication and dataset quality validation.
• Track model precision, recall, confusion patterns, confidence scores, and drift.
• Implement secure processing for customer communications containing sensitive data.

Qualifications:

• 4–6+ years of relevant machine learning, NLP, or data science experience.
• Proven experience deploying NLP models into production.
• Strong experience with classification systems and text analytics.
• Advanced Python development and testing skills.
• Hands-on experience with PySpark, SQL, pandas, and scalable data pipelines.
• Experience creating and validating labeled datasets.
• Strong understanding of model evaluation, monitoring, and false-alert reduction.
• Experience working with governed or PII-bearing data.

Nice to Have:

• Databricks
• Unity Catalog
• Databricks Workflows
• MLflow
• Model and data versioning
• Retrieval and embedding models
• LLM-assisted classification with evaluation and guardrails
• Contact-center or customer-support analytics
• Property-management or real-estate data experience