Data Scientist / Machine Learning Engineer
aKube Inc
Las Vegas, United States of America
5 days ago
Role details
Contract type
Temporary contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
Senior Compensation
$ 177KJob location
Las Vegas, United States of America
Tech stack
Data Deduplication
Python
Machine Learning
SQL Databases
Management of Software Versions
Large Language Models
Model Validation
Pandas
Build Management
PySpark
Machine Learning Operations
Text Analysis
Data Pipelines
Databricks
Job description
- 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.
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, * 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