Senior Data Scientist

Job description

Since 2003, Entrata has evolved from a visionary, student-led startup into a global leader in AI-driven property management technology. Today, we power the industry's most essential operating system, serving owners and residents worldwide through a comprehensive suite of intelligent leasing, payment, and communication tools powered by cutting-edge AI. With a proven track record of sustained growth and a global team of more than 2,200 employees, we offer the rare combination of established stability and high-velocity innovation. Recognized by the Silicon Slopes Hall of Fame and the Utah Business Fast 50, Entrata fosters a culture of radical transparency and entrepreneurial energy. At Entrata, we create an environment where different perspectives are valued and respected. Those perspectives challenge assumptions, strengthen our decisions, and raise the bar as we reshape the global living experience through AI-powered solutions. Following the launch of [**Forge**](https://www.entrata.com/blog/introducing-forge-a-language-model-built-for-multifamily), Entrata’s proprietary AI engine, we are looking for a **Senior Data Scientist** to elevate the performance and intelligence of our core models. You will lead experimentation around model adaptation, post-training, and error analysis, shaping how Forge processes complex operational workflows to deliver high-precision outcomes for property managers and residents worldwide. ### 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.