Machine Learning Engineer II

Our Opportunity:

The CFO organization at Chewy is undergoing a data-driven transformation to enhance financial operations and decision-making! As a Finance Analytics and ML Engineer, you’ll leverage sophisticated machine learning, large language models (LLMs), and AI to redefine forecasting, variance analysis, and financial data interpretation.

This role blends deep technical skills with financial acumen, enabling you to design intelligent systems that automate financial processes, detect anomalies, and explain variances between forecasted and actual outcomes. You’ll work closely with finance customers, data engineers, and AI teams to extract insights from structured and unstructured financial data in Snowflake and other enterprise platforms.

With a focus on predictive analytics and generative AI applications, you’ll play a pivotal role in shaping how Chewy's CFO organization understands and navigates financial performance. Does this sound like you? If so, we would love to hear from you! Come join the Pack! 

What you’ll do:

• Apply LLMs and generative AI techniques to enhance financial narrative generation, automate commentary for forecast variances, and surface contextual insights from structured and unstructured data.
• Build machine learning models to forecast key financial metrics, detect outliers, and support decision-making at scale.
• Create models and tools that explain the “why” behind mismatches in actuals vs. forecasts, empowering finance partners to take data-informed action.
• Develop and optimize data pipelines to transform Snowflake-based financial data into high-quality features for ML and AI applications.
• Partner with FP&A, Controllership, and other finance functions to capture requirements and ensure data products meet business needs.
• Ensure data quality, security, and governance practices are embedded into all engineering work.
• Continuously evaluate and incorporate new technologies to improve the automation, scalability, and performance of finance data systems.

What You’ll Need

• Bachelor’s degree in computer science, Engineering, or related field (Master’s preferred).
• 3+ years of experience in data engineering, with at least 1–2 years supporting machine learning applications.
• Proficiency with Python, SQL, and distributed data processing frameworks (e.g., Spark, dbt, Airflow).
• Experience working with cloud platforms (AWS, Azure, or GCP) and modern data warehouses (e.g., Snowflake, BigQuery).
• Familiarity with ML lifecycle tools (e.g., MLflow, SageMaker).
• A strong understanding of financial data, planning cycles, and forecasting processes is a plus.
• Excellent communication skills and the ability to translate technical insights into business value.