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We're looking for a Machine Learning Modeler to design and scale the next generation of intelligent systems that power data-driven decision-making across Block—from Cash App and Square to Corporate domains like Treasury, Cost, and Accounting. You'll build models and AI-driven workflows that don't just predict outcomes—they help shape them. Working across the full ML lifecycle, you'll transform raw data into foresight through advanced modeling, agentic AI workflows, and automation frameworks that enable faster, smarter decisions at scale. You'll partner closely with analytics and data science teams to bring experimental models into production and with finance and operations partners to build explainable, self-optimizing systems that make forecasts and insights transparent, actionable, and continuously learning.
Job Responsibility:
Design and implement forecasting, financial, or optimization models that power strategic decisions across Block
Build end-to-end ML pipelines for training, deployment, and monitoring, ensuring reproducibility and performance at scale
Collaborate with Data Science to productionize experimental models and integrate them into live systems
Partner with Analytics & Finance teams to ensure forecasts are interpretable, accurate, and aligned with business objectives
Develop or contribute to explainability tools that communicate model drivers, confidence, and uncertainty to stakeholders
Improve data pipelines and workflows using systems like Airflow, BigQuery, and Spark
Establish and document best practices for model evaluation, experimentation, and maintenance
Translate complex technical findings into clear, actionable recommendations for non-technical partners
Contribute to a culture of curiosity, high-quality engineering, and continuous learning within the Advanced Insights & Modeling organization
Requirements:
5+ years of experience in software or ML engineering, with hands-on experience delivering production-grade ML systems
Deep understanding of applied ML and forecasting, including time-series, regression, and value prediction modeling
Strong proficiency in Python and common ML libraries such as scikit-learn, XGBoost, LightGBM, and NumPy/pandas
Experience building data pipelines using tools such as Airflow, Spark, or similar orchestration systems, and working with BigQuery or other large-scale data warehouses
Familiarity with model explainability techniques (e.g., SHAP, feature attribution, uncertainty quantification)
Experience connecting model design to business objectives
Proven ability to work cross-functionally and drive high-impact results
Nice to have:
Experience in forecasting or planning models in fintech, consumer, or marketplace settings
Exposure to automated model serving, monitoring, or feedback loops in production
Background in statistical modeling, uncertainty estimation, or model interpretability research
A passion for transforming complex ML outputs into actionable insights and tools for decision-makers