Data Scientist (Machine Learning)

Nelo•New York, NY

1d•Onsite

About The Position

Nelo is a leading consumer fintech and e-commerce platform in Mexico, with >$500MM in annualized GMV and >$70MM in annualized revenue. Our mission is to increase the buying power of consumers in Latin America, and we are doing so by building a modern alternative to credit cards. Nelo has raised over $40M of venture capital from investors including Homebrew, Two Sigma Ventures and Susa Ventures. Nelo has additionally raised a $100M asset credit facility from Victory Park Capital. Our lean team includes experienced leaders from top technology companies including Uber, Amazon, Rappi, and DiDi. We pride ourselves on our velocity, intellectual rigor, and efficiency. Nelo has offices in Mexico City and New York City. Why this Role is Different Most Data Science roles currently on the market are focused on optimizing ad clicks or slightly improving recommendation engines. This isn't that. At Nelo, your models are the product. You are building the decision engine that determines who gets access to credit in an emerging market. This involves high-stakes constrained optimization problems where "good enough" mathematics will result in direct financial loss. We are looking for the type of person who is frustrated by the "black box" approach of modern libraries and actually understands the statistical theory and causality behind the code. If you want to apply academic-level rigor to a P&L that is scaling rapidly, this is your seat.

Requirements

You have deep theoretical roots. We are explicitly looking for candidates with a strong academic background (PhD preferred) who understand the first principles of classification, forecasting, and optimization.
You are a builder, not just a researcher. While you love the theory, you have at least 5 years of experience applying it in a production environment. You write production-grade Python and SQL.
You value velocity. You understand that a perfect model shipped next year is worth less than a great model shipped next week. You can balance intellectual rigor with the need to execute.
You are happy in NYC. This is an in-office role. We believe the hardest problems are solved when smart people are in the same room with a whiteboard.

Responsibilities

Solve the "Why," not just the "What": You will design and deploy causal inference models to drive our underwriting and portfolio management strategies. Correlation isn't enough when you're managing risk.
Build the Core Engine: You will create and refine the algorithms for credit pricing, personalization, and ranking. Your code will directly impact the wallet of the consumer and the margin of the company.
Own the Infrastructure: You won't just hand off a Jupyter notebook to an engineer. You will lead ML infrastructure projects, ensuring observability and operational excellence for the models you build.