Data Analyst

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.