Systems Data Analyst at Charles Schwab in Austin, TX

Summary: Charles Schwab is a financial services company that empowers its employees to make a significant impact on their careers. The Systems Data Analyst role involves supporting data-driven decision-making by analyzing and interpreting data related to Fraud, Anti-Money Laundering, and Financial Crimes Risk Management, leveraging SQL and AI-enabled analytics for improved outcomes. Responsibilities: - Extract, query, and analyze data from multiple fraud, AML and enterprise systems using SQL and related tools - Clean, validate, and transform raw data to ensure accuracy and consistency - Perform exploratory analysis to identify trends, patterns, gaps, and risks - Apply AI-enabled analytics tools or techniques (e.g., automation, pattern detection, anomaly identification) to improve analytical outcomes - Translate analytical findings into clear, actionable insights for non-technical audiences - Partner with business and technology teams to understand requirements and data needs - Support audits, risk assessments, and regulatory or management reporting as needed - Document data definitions, logic, assumptions, and AI usage to ensure transparency and repeatability Required Qualifications: - Bachelor's degree in Analytics, Data Science, Computer Science, Finance, or a related field - Strong proficiency in SQL and querying relational databases - Experience working with large datasets and multiple data sources - Experience using AI-enabled analytics tools (e.g., Python libraries, embedded AI in BI platforms, or automation frameworks) - Python or R for data analysis, automation, or AI-assisted workflows Preferred Qualifications: - Solid analytical and problem-solving skills - Ability to clearly communicate insights and findings to stakeholders - High attention to detail and data quality - Working knowledge of AI or advanced analytics concepts, such as: - using AI-assisted tools for data analysis or automation - basic understanding of machine learning concepts (e.g., classification, anomaly detection, trend analysis) - leveraging AI features within analytics platforms or programming tools Required Skills: SQL, Data Analysis, AI-enabled Analytics, Python, R, Data Cleaning, Data Validation, Financial Crimes Risk Management, Fraud Detection, Anti-Money Laundering (AML), Machine Learning Concepts