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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. Implements advanced machine learning and deep learning models, integrating them into end-to-end production workflows. Owns complete machine learning workflows with minimal technical supervision. Develops advanced feature engineering frameworks for large-scale personalization. Collaborates with Product Managers and cross-functional partners to translate business requirements into technical system designs. Builds and maintains CI/CD workflows for testing, validation, and production releases. Designs and implements data governance, validation, and monitoring mechanisms. Optimizes distributed processing performance and cloud resource utilization.
Responsibilities
- Designs, develops, and maintains production-scale distributed machine learning pipelines
- Implements advanced machine learning and deep learning models
- Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes
- Develops advanced feature engineering frameworks
- 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
- Designs and implements data governance, validation, and monitoring mechanisms
- Optimizes distributed processing performance and cloud resource utilization
Requirements
- 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
Skills
- 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
- Azure Databricks
- Apache Spark (PySpark)
- Delta Lake
- Distributed data processing
- CI/CD using GitHub Actions
- Cloud-based machine learning platforms
- eCommerce analytics
- Clickstream data analysis
- Customer behavior analytics
- Large-scale data integration and cleansing
- A/B testing methodologies
- KPI analysis
- Advanced feature engineering
- Supervised learning
- Unsupervised learning
- Python
- SQL
- Production-grade code development
- Cross-functional collaboration
Location
- US
Work Type
- Telecommuting
Education Level
- Master’s degree
Salary/Compensations
- $206,565 - $216,565