Data Scientist 2 at Reality Gap in San Francisco, CA

About the Role

Designs, develops, and maintains production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog 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 that combine multi-modal product attributes, customer behavioral signals, and temporal weighting logic to enable 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 using GitHub Actions and Databricks workflows to support testing, validation, and production releases of machine learning systems. Designs and implements 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 to balance model accuracy, processing latency, and infrastructure cost constraints. Telecommuting permissible from any location in the US.

Salary Range: $206,565 - $216,565 Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements.

What You'll Do

1. Transformer-based deep learning architectures, embedding-based modeling techniques, recommendation systems, personalization systems, collaborative filtering, content-based filtering, matrix factorization, re-ranking techniques, production-scale machine learning systems, MLOps practices, model lifecycle management, ML pipeline automation;

2. Azure Databricks, Apache Spark (PySpark), Delta Lake, and distributed data processing;

3. CI/CD using GitHub Actions;

4. Cloud-based machine learning platforms, eCommerce analytics, clickstream data analysis, customer behavior analytics, and large-scale data integration and cleansing;

5. A/B testing methodologies, KPI analysis, advanced feature engineering, supervised learning, unsupervised learning; and

6. Python, SQL, production-grade code development, cross-functional collaboration.

Who You Are

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