Data Analyst – Fraud Intelligence

Sardine

Data Analyst managing fraud detection analytics for Sardine's Fraud Intelligence team. Designing evaluation frameworks and partnering with stakeholders for actionable fraud intelligence.

Posted 7/1/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $115,000 - $145,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data analysis and statistical evaluation, with proficiency in SQL and Python or R for data manipulation and visualization. Capable of translating complex data findings into actionable recommendations while collaborating effectively with cross-functional teams in a fast-paced environment.

Highest-signal resume keywords

Data AnalysisSQL ProficiencyStatistical AnalysisVendor Data AssessmentCommunication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisStatistical AnalysisSQLPythonREvaluation MetricsA/B TestingLift AnalysisFraud DetectionData Profiling

Soft Skills

Written CommunicationVerbal CommunicationSynthesis of Complex AnalysisComfort with Ambiguity

Industry Keywords

FraudRiskFintechB2B SaaSData QualityMatch RatesPopulation CoverageFalse Positive RatesCatch RatesData Signals

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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