Member of Technical Staff - Fraud

1 мес. назад

175k–250k USD / yearUnited StatesMexicoWorldwideLeadOnsite

aifraud detectiondata analysis

Responsible for fraud detection and mitigation in a fintech lending platform handling over $500MM in annualized GMV.

Обязанности

Role mission: Engineer a fraud detection and mitigation system that maximizes precision first, then recall.
Fraud threat assessment. Within three weeks of start date, produce an independent fraud risk assessment of . Where are our biggest weaknesses? What is on fire, or a greater risk than we previously believed? What roadmap follows from those findings?
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.
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.
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.
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.

Требования

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 the role level and scope to the candidate, so do not rule yourself out if you have well above that.

Условия

Base salary: NYC $175-250K USD; CDMX adjusted for local cost of living.
Competitive equity.
100% medical, dental & vision insurance coverage for you (50% for dependents).
401(k) for US-based employees.
Unlimited PTO (most of us take about 3-4 weeks a year).
Extended maternity and paternity leave.
Relocation support.

Другое

Location: New York City or Mexico City, 80% in office
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.
has raised over $40M of venture capital from investors including Homebrew, Two Sigma Ventures, and Susa Ventures. 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.
has offices in Mexico City and New York City.
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 '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 is even possible six months from now depends on the controls you put in place.
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.
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 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.
~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 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.
At least two years managing fraud in tech or fintech, in a fraud analytics or fraud strategy seat with ownership of a fraud area and rules or models you deployed to production.
Advanced SQL comprehension. You may not write much of it yourself, but you must understand everything your agents write.
You work with AI agents in a meaningful capacity today.
Professional English.
You have built self-improving feedback loops for agentic systems.
Financial services background, especially exposure to first-party and acquisition fraud.
You have run point during live fraud incidents.
Working Spanish.
Conversation with the Hiring Manager
Case Study
On-site Interview
Reference & Background Check
Offer
I joined this company for two reasons:
The founders. Kyle and Stephen are world-class operators who brought the experimentation culture from Uber and applied it to a lending business in Mexico. The result after years of compounding is incredible; it must be near the top of its vintage (2019) for lending fintechs. The founders are kind, honest, hard-driving, paranoid (about risk!), pragmatic, intelligent, insanely hard-working, and never satisfied. I trust them with some of the best years of my career.
Unit economics and product-market fit. You cannot operate at our revenue run rate and be profitable on a net basis without the unit economics being solid, and without customers actually liking the product.
We have a senior and technical team, and a performance culture. This isn't financial advice, but I'll be honest, I'm excited about the value of my equity. Come build with the team and I!