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We're looking for a Senior Machine Learning Modeler to pioneer the design of intelligent systems that power the future of decision-making across Block—spanning Cash App, Square, and Corporate domains such as Treasury, Cost, and Accounting. You'll lead the architecture and delivery of AI-driven, self-adaptive models that forecast, reason, and act—shaping how Block allocates resources, scales growth, and plans for the future. This means going beyond traditional modeling: building autonomous ML workflows, graph-based retrieval systems (GraphRAG), and agentic orchestration frameworks (MCP and beyond) that make insights discoverable, explainable, and actionable across the company.
Job Responsibility:
Lead design and implementation of forecasting, financial, and cost modeling systems that inform company-wide decisions
Develop scalable ML architectures and pipelines for training, serving, and monitoring predictive models
Build and extend AI tools for model explainability and interpretability, making predictions accessible to Finance, Analytics, and Product teams
Partner with Data Science to operationalize research models, ensuring performance, reliability, and reproducibility
Collaborate with Forecasting Analytics and Corporate Finance teams to deliver insights that guide resource allocation and financial planning
Define technical standards, best practices, and frameworks for applied ML development across business lines
Requirements:
8+ years of experience in machine learning or software engineering, with proven experience leading large-scale ML projects
Deep expertise in forecasting, predictive modeling, and value estimation, including statistical and ML-based methods
Advanced proficiency in Python, and experience with libraries such as scikit-learn, XGBoost, LightGBM, and pandas/numpy
Strong experience building end-to-end ML pipelines, leveraging tools like Airflow, Spark, BigQuery, or equivalent systems
Demonstrated success in designing systems that support explainability, reproducibility, and operational reliability
Strong understanding of data modeling, feature engineering, and model evaluation in production contexts
Experience mentoring engineers and shaping team-wide technical direction
Nice to have:
Experience with forecasting frameworks (e.g., Prophet, statsmodels, or custom time-series methods)
Background in financial modeling, planning, or customer lifetime value prediction
Experience building automated or interactive explainability systems for ML-driven forecasts