You are a strategic thinker passionate about driving solutions in Business Analysis, Reporting and Analytics, and Fraud and Anomaly Detection. You have found the right team.

As a Business Analysis Associate I within our Corporate Investment Banking Payments Operations team, you will be responsible for improving operational efficiency, advancing strategic initiatives, and enabling service excellence through data-driven insights, automation, and strong cross-functional partnership. You will lead the Reporting and Analytics portfolio for fraud and anomaly detection across payments operations,delivering executive-ready reporting, dashboards, and ROI-driven business cases. You will analyze complex datasets and partner with Technology and Operations to deploy models and automation,strengthen controls, and improve risk outcomes and operational efficiency.

Job Responsibilities:

• Oversee end-to-end delivery for the Reporting and Analytics portfolio focused on fraud and anomaly detection across payments, reconciliations, and exceptions.
• Deliver ad-hoc and recurring reporting and produce executive-ready materials that quantify impact and performance.
• Prepare status updates, proposals, and business cases for senior leadership, articulating ROI from detection and automation initiatives.
• Analyze complex transaction and operations datasets using advanced analytics to identify patterns, outliers, and typologies and to test actionable hypotheses.
• Develop and apply statistical and machine learning models for classification, time series, and anomaly detection, communicating assumptions and outcomes to stakeholders.
• Collaborate with Technology and Operations to translate requirements into deployable capabilities, including testing, calibration, and case-management integration.
• Strengthen risk mitigation through root-cause analysis, continuous improvement, data quality controls, drift monitoring, and detection governance.
• Automate data ingestion, transformation, scoring, and alert routing by replacing manual workflows with reusable, well-tested scripts and jobs.
• Recommend prioritized insights and action plans, aligning cross-team deliverables and dependencies to achieve defined KPIs and outcomes.
• Build dashboards and visualizations that track risk and operational performance, enabling stakeholder transparency and self-service analytics.
• Coach and delegate tasks to support team capability-building, consistent execution, and a culture of continuous learning.

Required qualifications, capabilities and skills

• 4+ years of experience analyzing complex datasets and translating insights into business outcomes with strong stakeholder communication.
• Strong statistical foundation (hypothesis testing, probability, sampling, regression) and experience with imbalanced/noisy datasets.
• Proficiency in SQL, including complex joins, window functions, and performance tuning on large datasets.
• Hands-on proficiency in Python (e.g., pandas, numpy, scikit-learn) or equivalent for data preparation, modeling, and automation.
• Experience building and evaluating detection analytics (rules and ML), including feature engineering, tuning, thresholding, and metrics (precision/recall, ROC/PR AUC, F1).
• Ability to explain model decisions to non-technical audiences; familiarity with explainability and model governance concepts.
• Advanced Microsoft Excel (pivot tables, lookups, VBA/macros) and strong PowerPoint skills; working knowledge of Visio and Word.
• Understanding of business process analysis/modeling and the software delivery lifecycle (requirements, testing, deployment, change management).
• Experience working with large, multi-source datasets and designing reproducible, version-controlled workflows.

Preferred qualifications, capabilities and skills

• Cash Operations domain experience across payment rails and exception management; familiarity with fraud/anomaly detection typologies.

• Experience delivering automation that improves operational efficiency, alert triage, and investigator effectiveness.

• Exposure to AWS services (e.g., Athena, S3, Glue, Lambda); familiarity with Sage Maker/EMR, Spark/Py Spark, or Kinesis is a plus.

• Curiosity and aptitude for ML applied to business problems, including time series forecasting, anomaly detection, and rules-to-ML migration.

• Experience leveraging AI tools, including LLMs, to enhance analysis and productivity (e.g., summarization and narrative generation).

• Experience with BI/analytics tools (Tableau, Spotfire, Alteryx, Qlik/Qlik Sense) for dashboarding and analysis.

• Knowledge of modern data engineering practices, including orchestration, testing, and monitoring.