Possible Finance Offices

Own and build the company’s machine learning infrastructure, including a shared feature store, model-serving systems, drift monitoring, and unified deployment pipelines. Establish long-term MLOps standards and processes from the ground up, while collaborating with data scientists and engineers to drive adoption and improve production model reliability.

Own forecasting, budgeting, variance analysis, financial modeling, KPI reporting, and Board materials. Automate FP&A processes using AI, SQL, Databricks, and Sigma, while developing scenario analyses for product, pricing, capital structure, and financing decisions. Partner with Treasury, Capital Markets, Product, Accounting, Marketing, and executives on strategic financial initiatives, debt and equity decisions, liquidity, covenants, and enterprise value. Translate complex analysis into clear recommendations for leadership and the Board.

Own data science for payment performance, optimization, timing, retry strategies, monitoring, experimentation, and fraud detection. Build payment-health scorecards, anomaly monitoring, payment-risk models, and production ML systems using Python, SQL, PySpark, Databricks, and MLOps tooling. Partner with Engineering, Product, and Risk to shape payment strategy and roadmap while applying causal inference, feature engineering, and production model monitoring.