About the Role
You will own an end-to-end machine learning project, from data exploration and feature engineering through training, benchmarking, deployment, and monitoring. You will apply LLMs, deep learning, gradient boosting, causal inference, A/B testing, and statistical methods to solve problems in credit, fraud, growth, or spend management. You will collaborate with product and business stakeholders to turn models and insights into user-facing features and strategy.
Requirements
•Currently pursuing a degree in Data Science, Computer Science, Math, Physics, Economics, Statistics, or another quantitative field with an expected graduation date between December 2027 and 2029
•Understand machine learning, statistics, probability, and optimization
•Have interest or experience integrating LLMs and agents into applied solutions
•Use Python and data science libraries including pandas, scikit-learn, NumPy, and PyTorch
•Use SQL to wrangle data in a modern data warehouse
•Have experience curating datasets and building and evaluating machine learning models
•Communicate complex concepts to technical and non-technical audiences
Responsibilities
•Own the machine learning lifecycle from data exploration and feature engineering through training, benchmarking, deployment, and monitoring
•Leverage large language models to solve novel problems and create product capabilities
•Apply machine learning techniques including deep learning, gradient boosting, and causal inference
•Quantify impact through A/B tests and statistical methods
•Partner with product and business leaders to translate models and insights into strategy and user-facing features
Benefits
•Apple MacBook
•Catered lunches in the NYC office Monday through Friday
•Weekly coffee stipend
•Housing stipend
