Data Analyst - Fraud Intelligence

Sardine•,

19h•$115,000 - $175,000•Remote

About The Position

We’re looking for a Data Analyst to join Sardine’s Fraud Intelligence team. This role sits at the intersection of data evaluation, vendor strategy, and fraud detection. You’ll be the analytical engine behind how we assess, test, and onboard new third-party data signals and vendor partnerships — determining which data assets actually move the needle on fraud outcomes for our clients. This is a high-ownership, high-visibility role. You’ll work closely with the Head of Fraud, product, data engineering, and client-facing teams to build rigorous testing frameworks and translate raw vendor data into actionable fraud intelligence.

Requirements

3–5 years of experience in data analysis, data science, or a related analytical role — ideally in fraud, risk, fintech, or a data-heavy B2B SaaS environment
Proficiency in SQL (required) and Python or R for data manipulation, statistical analysis, and visualization
Solid understanding of evaluation metrics and statistical concepts: precision/recall, AUC/ROC, lift, population distributions, and A/B testing basics
Experience working with external or third-party datasets — assessing data quality, match rates, and signal value
Strong written and verbal communication skills; ability to synthesize complex analysis into clear narratives for non-technical stakeholders
Comfort with ambiguity and the ability to define your own structure in a fast-moving environment

Nice To Haves

Familiarity with fraud signals and data types: device fingerprinting, identity graph data, consortium data, behavioral signals, email/phone intelligence
Experience in a vendor evaluation, data partnerships, or procurement-adjacent analytical role
Exposure to machine learning concepts and feature engineering, even if not in a full ML engineering capacity
Experience working across fintech verticals such as crypto, BNPL, neobanks, or payments

Responsibilities

Design and execute structured evaluation frameworks to assess the quality, coverage, and fraud-signal value of incoming data assets from vendor partners
Build lift analyses, backtests, and champion/challenger comparisons to quantify the incremental value of new data signals against our existing fraud detection stack
Profile vendor datasets for completeness, freshness, match rates, and population coverage across verticals (crypto, fintech, neobanks, e-commerce, etc.)
Collaborate with fraud leadership to define evaluation criteria tied to real fraud outcomes — false positive rates, catch rates, precision/recall tradeoffs
Translate vendor data findings into clear, actionable recommendations: adopt, pilot, deprioritize, or decline
Partner with data engineering to define ingestion requirements and ensure test environments reflect production-like conditions
Document evaluation results and maintain an internal knowledge base on vendor data performance over time
Support ad hoc deep dives into fraud trends, model performance, and client-specific data questions as needed

Benefits

Generous compensation in cash and equity
Early exercise for all options, including pre-vested
Work from anywhere: Remote-first Culture
Flexible paid time off and Year-end break
Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific
4% matching in 401k / RRSP - US and Canada specific
MacBook Pro delivered to your door
One-time stipend to set up a home office — desk, chair, screen, etc.
Monthly meal stipend
Monthly social meet-up stipend
Annual health and wellness stipend
Annual Learning stipend