Senior Data Analyst

Role: Senior Data Analyst with Finance and Payment Location: Sunnyvale ,CA (Onsite ) Type: Contract Focusing on payment risk management and financial services and analytics / basic data science. Preferred candidate should have some prior banking, risk management experience with strong analytical and communication skills. Job description : Key Responsibilities Senior / 5 years They want an owner, not just a doer. You should be able to: • Work with minimal supervision. • Mentor junior analysts. • Lead small projects or analyses from start to finish. • Translate data findings into actionable business recommendations. Analytics / Data Science The core of the job is problem-solving with data. • Analysis: Defining metrics, building reports, monitoring trends, performing root-cause analysis (e.g., "Why did fraud spike last Tuesday?"). • Data Science (Predictive): Building models (likely in Python) to predict fraud risk (e.g., ML models for transaction scoring). The 20% Python suggests maintaining or improving existing models, not necessarily building new ones from scratch. Risk Deep understanding of financial risk, specifically: • Fraud: First-party (friendly) fraud, third-party fraud, account takeover. • Credit Risk: If the role involves lending or buy-now-pay-later products. • Compliance: AML (Anti-Money Laundering), KYC (Know Your Customer) regulations. • Payment Strategy: Decline analysis, false positives, optimizing acceptance rates. Payment Industry-specific knowledge is critical. You must understand: • The payment flow: authorization, authentication, settlement. • Key players: acquirers, issuers, card networks (Visa, MC). • Key concepts: chargebacks, reversals, interchange fees, 3D Secure. Finance Understanding the financial impact of your work. You need to calculate: • Cost of fraud (lost merchandise + fees). • ROI of fraud prevention tools. • The balance between reducing fraud and negatively impacting good customer sales (false positives). SQL - 80% This is the primary tool. You need to be an expert. • Advanced SQL: Complex joins, CTEs, window functions (RANK, LAG), query optimization. • Data Extraction: Pulling large, clean datasets for analysis. • Dashboarding: Building core reports and dashboards (likely in Tableau, Looker, Mode, etc.) for the team. Python - 20% Used for more advanced analysis beyond SQL's capabilities. • Libraries: Pandas (data manipulation), Scikit-learn (machine learning), NumPy, Matplotlib/Seaborn (visualization). • Use Cases: Building logistic regression models, random forests, or gradient boosting for risk scoring. Performing cohort analysis or network analysis. Communication The most important "soft skill." You must bridge the gap between technical data and business decisions. • To Stakeholders: Explain complex risk concepts and data findings to non-technical leaders (e.g., Heads of Product, Finance, CRO). • To Engineers: Clearly define requirements for new data tracking or fraud rule implementation. • Documentation: Writing clear summaries of your analysis and recommendations.