Senior Scientist Jobs

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About The Position

This role focuses on building and deploying Natural Language Processing (NLP) classification models for customer communications. The Senior Data Scientist / Machine Learning Engineer will be responsible for developing taxonomies for intent, topic, and sentiment, cleaning and preparing data, and implementing trend and anomaly detection methods. The position also involves designing data pipelines, creating labeled datasets, and monitoring model performance, including precision, recall, confidence scores, and drift. A key aspect of the role is ensuring secure processing of sensitive customer data.

Requirements

  • 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
  • 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 Haves

  • 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

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.

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