> Markdown version of [/jobs/ext/1343725-data-scientist-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/1343725-data-scientist-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist / Machine Learning Engineer

- **Company:** aKube Inc
- **Location:** Las Vegas, NV, United States
- **Experience:** Expert
- **Salary:** $176,800.0
- **Contract:** Temporary contract
- **Skills:** Data Deduplication, Python (Programming Language), 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
- **Published:** July 19, 2026
- **Apply:** https://www.dice.com/job-detail/8312287b-fb25-4676-bb1b-48c553ef4454

## About the Role

* 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

## 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.


## Related Videos

- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source)
- [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)
- [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)