Senior Data Scientist

We are seeking a Senior Data Scientist to help improve the quality and performance of Entrata’s AI models and applications. This role will focus on fine-tuning strategy, training data, experimentation, evaluation, and identifying the approaches that produce the best outcomes for complex property management workflows.

Responsibilities:

• 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.

Minimum Qualifications:

• 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.

Preferred Qualifications:

• 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.