Senior Data Scientist / Machine Learning Engineer, NLP - 1635

aKube•Calabasas, CA

1d•Onsite

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.