Data Scientist 2

The Gap • Folsom, CA

2d • $206,565 - $216,565 • Remote

About The Position

Designs, develops, and maintains production-scale distributed machine learning pipelines to support e-commerce recommendation systems across multiple brands and markets. Implements advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems, and integrates them into end-to-end production workflows.

Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes, operating with minimal technical supervision. Develops advanced feature engineering frameworks combining multi-modal product attributes, customer behavioral signals, and temporal weighting logic for large-scale personalization.

Collaborates directly with Product Managers and cross-functional partners to translate business requirements into technical system designs, define success metrics, and document implementation constraints. Builds and maintains CI/CD workflows and deployment automation to support testing, validation, and production releases of machine learning systems. Designs data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets. Optimizes distributed processing performance and cloud resource utilization while balancing model accuracy, processing latency, and infrastructure cost constraints.

Telecommuting is permissible from any location in the US. Salary range: $206,565–$216,565. Employee pay varies based on qualifications, experience, skill level, competencies, and work location. The employer will meet the minimum wage or minimum of the pay range, whichever is higher, based on city, county, and state requirements.

Requirements

• Master’s degree in Data Science, Computer Science, Computer Information Systems, or a related field.
• Four years of work experience in the job offered or a related role.
• Experience or knowledge in transformer-based deep learning architectures, embedding-based modeling, recommendation and personalization systems, collaborative filtering, content-based filtering, matrix factorization, re-ranking, production-scale ML systems, MLOps, model lifecycle management, and ML pipeline automation.
• Experience or knowledge with Azure Databricks, Apache Spark (PySpark), Delta Lake, and distributed data processing.
• Experience or knowledge with CI/CD using GitHub Actions.
• Experience or knowledge with cloud-based machine learning platforms, e-commerce analytics, clickstream data analysis, customer behavior analytics, and large-scale data integration and cleansing.
• Experience or knowledge with A/B testing methodologies, KPI analysis, advanced feature engineering, supervised learning, and unsupervised learning.
• Python, SQL, production-grade code development, and cross-functional collaboration.

Responsibilities

• Design, develop, and maintain production-scale distributed machine learning pipelines for e-commerce recommendation systems across multiple brands and markets.
• Implement advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems, and integrate them into production workflows.
• Own machine learning workflows spanning data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes.
• Develop feature engineering frameworks combining multi-modal product attributes, customer behavioral signals, and temporal weighting for large-scale personalization.
• Collaborate with Product Managers and cross-functional partners to translate business requirements into technical designs, define success metrics, and document implementation constraints.
• Build and maintain CI/CD workflows and deployment automation for testing, validation, and production releases.
• Design data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets.
• Optimize distributed processing performance and cloud resource utilization in consideration of model accuracy, latency, and infrastructure costs.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level