Member of Technical Staff - Fraud

About this role

MEMBER OF TECHNICAL STAFF - FRAUD

Location: New York City or Mexico City, 80% in office

ABOUT NELO

Nelo is a (profitable) leading consumer fintech and e-commerce platform in Mexico, with >$500MM in annualized GMV and >$70MM in annualized revenue. Our mission is to increase consumers' buying power in Latin America, and we're doing so by building an AI-native platform powered by credit.

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 such as 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

In most companies, fraud is a cost center. You inherit a rules engine someone else built, you tune thresholds inside guardrails someone else set, and success means losses stayed flat.

The mandate here is bigger. In your first three weeks you will run an independent assessment of Nelo's entire fraud surface and write the roadmap yourself. You will own detection, mitigation, and the operating function behind them, end to end, on a lending business moving more than $500MM in annualized GMV.

The mandate is precision first: we would rather challenge almost nobody and be right than blanket good users with friction. You will build the fraud ops function as an agent-operated system from the start, and you will work directly with the founders. Whether a programmatic attack against Nelo is even possible six months from now depends on the controls you put in place.

WHAT YOU'LL DO

Role mission: Engineer a fraud detection and mitigation system that maximizes precision first, then recall.

1. Fraud threat assessment. Within three weeks of start date, produce an independent fraud risk assessment of Nelo. Where are our biggest weaknesses? What is on fire, or a greater risk than we previously believed? What roadmap follows from those findings?

2. Step-up optimization. Best-in-class log-in challenge rates are ultra-high precision and very low in relative terms. Within five weeks of start date, launch a test intended to get us to our challenge rate and precision goals.

3. Acquisition fraud (if your background is in financial services). Much of our fraud in dollar terms is first-party fraud at time of acquisition: a tough problem, and one rich in opportunity. Within one month of start date, launch an experiment that reduces first payment default rate by 3 percentage points for a segment, with the smallest possible impact to approval rate.

4. Establish the fraud ops function. Within three months of start date, build a system that generates repeatable intelligence from a manual fraud case review feedback loop, whether with AI agents, human agents, or both.

5. Cap any currently uncapped fraud risk. Within six months of start date, the risk of an unmitigated, programmatic fraud attack (acquisition, transaction, ATO) should be reduced to negligible because of controls you have put in place. This is the number one outcome for this role.

WHY YOU SHOULD APPLY

- You have owned a fraud area before: reviewing cases in your domain, deploying high-precision rules or models to production, and iterating until the fraudsters give up and go bother someone else.

- You run AI agents in a meaningful capacity today, and you are building toward self-improving feedback loops for agentic systems.

- Your analysis stands on its own. Nobody double-checks your work for technical correctness, and you understand every line of SQL your agents write.

- You step through every screen a user will see before a control ships. You can intuit what good and bad product experiences look like, for good users and for fraudsters.

- You can take charge during an incident, tell people exactly what to do to close the gap on an active threat, and persuade the room with evidence when the fix is unpopular. People like working with you, which makes the persuasion stick.

- You are legitimately paranoid about new threat vectors, and it shows in your work.

WHY YOU SHOULD NOT APPLY

- You want a mature fraud stack with an established playbook. Most of this does not exist yet. You are building the function, not stepping into one.

- Your background is purely fraud data science or purely fraud ops. This role sits in fraud strategy and analytics with production ownership, and it needs both the judgment and the shipping.

- You have not started working with AI agents, or you think they are a gimmick. The fraud ops function you build will be agent-operated from day one, and every team at Nelo uses AI daily.

- You prefer setting rules from a distance and letting the consequences follow. We walk every user-facing flow before shipping a control.

- You want a machine that is already fully optimized. We are discovering the correct credit box as we go, and that takes a lot of work. If that is unpalatable to you, that is fine, but this role is not for you.

- You cannot commit to 80% in office in New York City or Mexico City. Incident response and proximity to Risk and Engineering make this non-negotiable.

HOW WE WORK

- ~60 people across CDMX and New York. Lean, fast, opinionated about quality.

- This role is based in New York City or Mexico City and is expected to be 80% in office given the proximity required with Risk, Engineering, and incident response.

- Every team at Nelo uses AI in daily workflows. Fraud is no exception.

- We are dealing with people's financial lives. If you mess up an analysis, fix it and move on, but never obfuscate a mistake.

WHO YOU ARE

A note on experience: there is no general years-of-experience requirement. To have the competencies below and work within the posted comp range, we would expect a minimum of about five years total. We can flex

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