À propos de ce poste Data Scientist (Machine Learning) chez Nelo
About Nelo
Nelo is a leading fintech in Mexico reimagining credit to expand consumer buying power. Founded in 2019 by former Uber international growth team leads Kyle Miller and Stephen Hebson, Nelo offers credit cards, BNPL loans, bill payments, and its own marketplace — all under one app — designed from first principles for a mobile-first, real-time payments world.
With $100M in annual revenue, $1B in annualized GMV, and $140M+ raised in equity and debt, Nelo is one of the most capital-efficient consumer fintech companies in Latin America. The company is profitable — positive operating profit in both 2024 and 2025. It runs lean and technical: 60 employees, 8 engineers, with AI driving underwriting, collections, and operations.
HQ: Mexico City, Mexico; commercial and people operations in New York City
Founded: 2019
Team size: 60 FTEs
Open roles: 10+
Website: nelo.mx
Stage: Post-Series A; $100M credit facility (Victory Park Capital, 2022); profitable
Benefits: 401k, open PTO, medical, dental, vision, STD, LTD, fertility
Company values:
Be your best selves: ambition, hard work, intellectual curiosity
Everyone is an owner: all employees receive equity including customer support; 10-year option expiration; equity refreshers
Customer first: product decisions grounded in customer benefit
Open and honest communication: quarterly financials and board presentations shared company-wide; low tolerance for bureaucracy or gossip
Move fast, learn fast: hundreds of experiments run monthly; rigorous experimentation culture
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.
What You'll Do:
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.
Who You Are:
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.
What's on the Table
Significant Equity (You’re building the company, you should own it).
100% medical, dental & vision insurance coverage for you (50% for dependents).
Unlimited PTO (that we actually expect you to take).
401(k).
Extended maternity and paternity leave.
Relocation support and Sabbatical program.
About the Process
We know you're busy, so we don't do 8-stage interviews.
Quick chat with the Hiring Manager to align on expectations.
A business case/technical assessment (relevant to the actual job).
Onsite interview in NYC to meet the team.
Offer.
