Senior Data Scientist

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.