Senior P2P Risk Strategy Analyst

We are seeking a highly analytical and data-driven P2P Risk Strategy Specialist to design, optimize, and scale our risk automation framework. In this role, you will be responsible for protecting our platform against peer-to-peer transaction risks, including account takeovers, social engineering scams, money laundering, and payment velocity abuse.

You will combine a strong Data background with hands-on coding in Python and SQL to detect subtle fraud patterns across massive transaction ecosystems. 

• Pattern Detection & Behavioral Analysis: Mine high-volume, multi-dimensional transaction and event logs to uncover emerging P2P fraud vectors, synthetic networks, collusion rings, and anomalous transfer patterns.

• Risk Strategy Development: Design, implement, and iterate real-time decisioning rules and risk policies (e.g., dynamic transfer limits, step-up authentication, cooling-off periods) to mitigate risk while preserving a frictionless user experience.

• Advanced Data Analysis: Write production-grade, optimized SQL and Python scripts to analyze network graphs, transaction velocity, device signals, and user interaction metrics.

• Model Integration & Experimentation: translate models into operational strategies, establish decision thresholds, and run A/B tests to quantify strategy performance.

• Post-Mortem & Loss Mitigation: Perform quantitative root-cause analysis on fraudulent transactions, chargebacks, and ATO incidents to plug strategy gaps and minimize direct financial losses.

• Risk Performance Monitoring: Build and maintain automated dashboards and KPI tracking systems

Core Requirements:

• 3+ years of experience in risk strategy, transaction monitoring, payment fraud analysis, or trust & safety within Fintech, Banking, or Digital Marketplaces.

• Advanced SQL Skills: Proficient in complex joins, window functions, CTEs, and optimizing queries on large-scale distributed databases (e.g., Snowflake, BigQuery, Redshift).

• Proficient in Python: Strong experience using Python libraries (pandas, numpy, scikit-learn, networkx) for data manipulation, exploratory data analysis, and rule automation.

• Pattern Detection & Analytics: Proven track record of identifying complex fraud networks, transaction velocity anomalies, or multi-account abuse patterns.

Preferred Experience & Education:

• Educational Background: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Economics, or a related quantitative field (Data Science background strongly preferred).

• Direct experience with P2P payment mechanisms, real-time settlement rails, card-to-card transfers, or digital wallets.

• Hands-on experience with graph theory/network analysis (identifying linked accounts, shared device fingerprints, IP linkages).

• Familiarity with machine learning workflows, rule engine architectures, and dynamic risk-scoring frameworks.

• Strategic Problem Solver: Comfortable tackling ambiguous, rapidly evolving risk problems in a fast-paced environment.

• Cross-Functional Communication: Excellent at translating data science insights and technical risk concepts into strategic recommendations for business stakeholders.