Data Scientist I - Full Stack Management Trainee
Headquartered in San Diego, Mulligan Funding serves as a leading provider of working capital (up to $10M) to small and medium-sized businesses. Since 2008, we have prided ourselves on our collaborative, innovative, and customer-focused approach. We are transforming small business lending by replacing legacy processes with fast, intelligent, AI-driven decisioning. Backed by 18 years of proprietary credit data and deep risk expertise, our production-grade AI agents are already running in active credit and underwriting workflows. As we expand this AI-first approach across Sales, Customer Lifecycle, Finance, and Capital Markets, we offer an uncommonly rich environment for emerging data scientists.
About the role
This Data Scientist I - Full Stack Management Trainee role focuses on machine learning development, model deployment, and MLOps. Working alongside senior engineering and data science leads, you will take ownership of model construction, experimental design, pipeline development, and code productionalization on cloud infrastructure, making an immediate impact on our production systems.
Responsibilities
- Assist in constructing, testing, and deploying machine learning models
- Design and evaluate experimental designs and A/B testing methodologies
- Productionalize data science code utilizing GitHub, version control, and modern MLOps pipelines
- Work with data vendors in pushing data boundaries
Requirements
- Master’s degree or higher in Mathematics, Statistics, or a Quantitative field
- Hands-on experience with A/B testing methodologies and experimental design
- Proven experience building ML models and exposure to MLOps principles
- Ability to productionalize code using GitHub and manage code versioning
Pay
A reasonable estimate of the base salary range for this role is $90,000 to $107,300 per year. In determining final compensation within the base range, Mulligan Funding considers a variety of factors, including market data, relevant experience, skills, and past performance.