Financial Data Modeler (Transactions & Banking Analytics)

What You'll Do: Build and maintain data models for debit/credit transaction flows, including ACH, wires, card, check, and internal transfers. Develop cash flow, liquidity, and balance forecasting models using historical transaction patterns. Design reconciliation logic between transaction systems, sub-ledgers, and the general ledger, and flag breaks and anomalies. Create models to detect unusual activity (fraud, AML patterns, duplicate or erroneous postings) using statistical and machine learning methods. Apply AI/ML and generative AI tools to automate data preparation, categorization, exception handling, and model documentation. Partner with Treasury, Finance, Risk, Compliance, and Operations to translate business questions into model requirements. Document models, assumptions, and validation results in line with model risk management standards (e.g., SR 11-7).Build dashboards and reporting that make model outputs clear to non-technical stakeholders. Ensure data quality, lineage, and governance across transaction datasets.

What You Bring: 3+ years of experience in financial data modeling, analytics, or quantitative work, preferably at a bank, custodian, asset manager, or fintech. Solid understanding of banking transactions: debits, credits, posting logic, settlement, accruals, and double-entry accounting. Strong SQL and Python (pandas, Num Py, scikit-learn or similar).Experience with cloud data platforms such as Snowflake, Databricks, Big Query, or Azure/AWS.Hands-on experience applying AI/ML to financial data, from classification and anomaly detection to time-series forecasting. Advanced Excel and a BI tool (Power BI or Tableau).Ability to explain complex models clearly and work cross-functionally. Bachelor's degree in Finance, Economics, Statistics, Computer Science, Data Science, or a related field.

Nice to Have: Familiarity with payment standards and rails: ISO 20022, SWIFT, Fedwire, NACHA/ACH.Experience with core banking or financial platforms (FIS, Fiserv, Temenos, SAP, Oracle Financials) or market data tools (Bloomberg).Exposure to custody, asset servicing, treasury, or wealth management operations. Knowledge of regulatory frameworks such as CCAR/DFAST, CECL, BCBS 239, or AML/BSA.Experience with dbt, Airflow, Git, or other modern data engineering tools. Experience using LLMs or AI copilots to speed up model development and documentation. CFA, FRM, or a relevant data/cloud certification.