Data Analyst

Data Analyst on the Fraud Intelligence team responsible for evaluating third-party vendor data signals through lift analyses, backtests, and statistical frameworks to improve fraud detection outcomes. Requires 3-5 years analytical experience, strong SQL and Python/R skills, and expertise in metrics like precision/recall and A/B testing, ideally in fintech or fraud.

About the job

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.

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.