Data Scientist I - Full Stack Management Trainee

Socket.dev; San Diego (CA); On-site; USD 85,000 - 120,000; Full time

Job summary

Mulligan Funding is seeking a Data Scientist I - Full Stack Management Trainee to advance ML development and model deployment on cloud infrastructure. You will work with senior leads to design experiments, build production-grade ML workflows, and contribute to scalable data science solutions across teams.

Join a fast-growing, AI-driven environment where hands-on model construction, evaluation, and deployment are the focus, with emphasis on modern MLOps practices and GitHub-based collaboration.

Qualifications

• 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.
• Ability to productionalize code using GitHub and manage versioning.

Responsibilities

• Assist in constructing, testing, and deploying machine learning models.
• Design and evaluate experimental designs and A/B testing methodologies.
• Productionalize data science code using GitHub, version control, and modern MLOps pipelines.
• Collaborate with data vendors to push data boundaries.

Skills

A/B testing

Experimental design

ML model development

Productionalize code

Education

Master's degree or higher in Mathematics/Statistics/Quantitative field

Tools

GitHub

MLOps pipelines

Job description

Headquartered in San Diego, Mulligan Funding serves as a leading provider of working capital (Up to $10M) to the small and medium-sized businesses that fuel our country. Since 2008, we have prided ourselves on our collaborative, innovative, and customer-focused approach. Enjoying a period of unprecedented growth, driven by the combination of cutting-edge technology, human touch, and unwavering integrity, we are looking to add to our people first culture, with highly motivated and results-oriented professionals, to push the limits of what’s possible while creating value for all of our partners.

At Mulligan, 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.

By stepping directly into the center of these efforts, you won't just observe modern machine learning—you will gain hands-on experience with advanced, industry-leading tools and complex technical architectures while contributing directly to our mission of scaling AI across the organization.

The 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.

You will:

• 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.

Qualifications & Requirements

• Education: Master's degree or higher in Mathematics, Statistics, or a Quantitative field.
• Statistical Expertise: Hands-on experience with A/B testing methodologies and experimental design.
• ML & Engineering: Proven experience building ML models and exposure to MLOps principles.
• Production Skills: Ability to productionalize code using GitHub and manage code versioning.

Mulligan Funding is an Equal Opportunity Employer (EOE) and takes great pride in building a diverse work environment. Qualified applicants are considered for employment without regard to age, race, religion, gender, national origin, sexual orientation, disability or veteran status.