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;
- 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.