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# Data Scientist

- **Company:** Lever, Inc.
- **Location:** Lehi, UT, United States
- **Experience:** Expert
- **Salary:** $133,100.0 - $213,200.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Big Data, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Supervised Learning, Large Language Models, Model Validation, Information Technology, Machine Learning Operations
- **Published:** August 26, 2026
- **Apply:** https://jobs.lever.co/entrata/52b9ae4f-553a-4f10-92cb-90173595ea82/apply

## About the Role

* 5+ years of experience in data science, machine learning, applied AI, or a related field.
 * Hands-on experience working with large language models, including fine-tuning, evaluation, or model adaptation.
 * Strong proficiency in Python and common machine learning frameworks.
 * Experience designing experiments, analyzing model performance, and working with large datasets.
 * Strong understanding of supervised learning, model evaluation, and statistical analysis.
 * Experience building or evaluating machine learning systems in production environments.
 * Ability to communicate technical findings clearly to engineering, product, and business stakeholders., * Experience with supervised fine-tuning, preference optimization, or other LLM post-training techniques.
 * Experience creating synthetic training data or model-generated datasets.
 * Experience building LLM evaluation frameworks, benchmark suites, or automated quality measurement systems.
 * Familiarity with agentic AI systems, tool use, and retrieval-based applications.
 * Experience working with enterprise, financial, legal, operational, or other domain-specific AI applications.
 * Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience.

## Description

* Fine-tune and evaluate foundation models for Entrata-specific use cases using supervised fine-tuning and other post-training methods.
 * Design and curate high-quality training datasets, including instruction data, preference data, and synthetic data.
 * Develop evaluation frameworks and benchmarks to measure model accuracy, reasoning, reliability, and task performance.
 * Conduct experiments to determine which models, datasets, prompts, and training approaches perform best for specific use cases.
 * Perform model error analysis and identify opportunities to improve model behavior and output quality.
 * Partner with machine learning engineers to move successful experiments into production.
 * Develop approaches for measuring and improving model safety, consistency, and enterprise readiness.
 * Translate business and product problems into measurable machine learning objectives.


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