Design and deliver production-ready machine learning systems for e-commerce recommendations across multiple brands and markets.

Design, develop, and maintain production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog.
Build and productionize advanced ML and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems.
Own end-to-end ML workflows: data ingestion, feature engineering, model training, inference, versioning, and automated refresh with minimal technical supervision.
Develop feature engineering frameworks that combine multi-modal product attributes, customer behavioral signals, and temporal weighting logic for large-scale personalization.
Collaborate with Product Managers and cross-functional partners to translate business requirements into technical system designs, define success metrics, and document implementation constraints.
Build and maintain CI/CD and deployment automation using GitHub Actions and Databricks workflows to support testing, validation, and production releases.
Design and implement data governance, validation, and monitoring to support data quality, lineage, reliability, and compliance for production ML assets.
Optimize distributed processing performance and cloud resource utilization to balance model accuracy, processing latency, and infrastructure cost.

Master’s degree in Data Science, Computer Science, Computer Information Systems, or related field.
4 years of work experience in the job offered or a related role.

Transformer-based deep learning architectures and embedding-based modeling techniques.
Recommendation and personalization systems, including collaborative filtering, content-based filtering, matrix factorization, and re-ranking techniques.
Production-scale machine learning systems using MLOps practices and model lifecycle management.
ML pipeline automation and distributed data processing with Azure Databricks, Apache Spark (PySpark), and Delta Lake.
CI/CD using GitHub Actions.
Cloud-based ML platforms with eCommerce analytics and clickstream/customer behavior analytics.
Experimentation and measurement using A/B testing and KPI analysis.
Advanced feature engineering and training approaches across supervised learning and unsupervised learning.
Production-grade code development and cross-functional collaboration.

Merchandise discount: 50% off regular-priced merchandise at Old Navy, Gap, Banana Republic, and Athleta; 30% off at Outlet for all employees.
One of the most competitive Paid Time Off plans in the industry.*
Up to five “on the clock” hours each month to volunteer for a charity of choice.*
Extensive 401(k) plan with company matching for contributions up to four percent of an employee’s base pay.*
Employee stock purchase plan.*
Medical, dental, vision, and life insurance.

Location: San Francisco, CA (remote)

Compensation: USD 206,565 - 216,565 per year

Data Scientist 2

Job Description