Note: The job is a remote job and is open to candidates in USA. Sardine is the leading agentic risk platform for fighting financial crime, and they are seeking a Data Analyst to join their Fraud Intelligence team. The role involves evaluating data signals from vendor partnerships to enhance fraud detection and collaborating with various teams to translate raw data into actionable insights.

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

Skills

• 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
• 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

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

Company Overview

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