Data Scientist I - Full Stack Management Trainee
Summary
Build and deploy ML models for fintech underwriting and lending workflows using MLOps pipelines and cloud infrastructure.
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.
As published by lever
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