Description

Build, test, and deploy recommendation, personalization, and predictive models that power digital experiences across banners.

This role is hybrid.

Responsibilities

• Develop end-to-end ML pipelines from data preparation and feature engineering to model training, evaluation, and production inference.
• Leverage Hugging Face Transformers, embedding models, and semantic search techniques to enhance product relevance and contextual understanding.
• Explore generative AI and retrieval-augmented generation (RAG) approaches for product matching, search, and personalization.
• Collaborate with MLEs to productionize models and ensure scalable, monitored, and retrainable ML systems.
• Partner with Data Engineers to design efficient data flows and maintain clean, versioned datasets for model consumption.

Required Skills

• 5+ years of experience in data science or machine learning engineering.
• Proficiency in Python, PySpark, and Databricks for large-scale data processing.
• Strong experience with scikit-learn, PyTorch, TensorFlow, and Hugging Face libraries.
• Proven background in recommendation systems, semantic search, or customer personalization models.
• Experience in feature engineering, EDA, model evaluation, and MLOps workflows.
• Familiarity with A/B testing frameworks and model observability practices.

Preferred Skills

• Bachelor's degree in Computer Science, Statistics, or related field.
• Strong collaboration and communication skills to work effectively with cross-functional teams.
• Experience contributing to shared best practices and documentation for advanced analytics ecosystems.

Key Skills

Education

Bachelor's degree