What are the responsibilities and job description for the Senior Data Scientist / Machine Learning Engineer, NLP - 1635 position at aKube Inc?
City: Las Vegas, NV / Calabasas, CA
Onsite/ Hybrid/ Remote: Onsite 4 days a week
Duration: 6 months
Work Authorization: GC, USC, All valid EADs except H1B, OPT, CPT
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