Description

You will design, develop, and implement deep learning-based recommender systems for grocery retail and e-commerce personalization.

This role is on-site.

Responsibilities

• Build advanced machine learning and deep learning models for product, coupon, substitute, and recipe recommendations.
• Define evaluation methods, key metrics, and A/B testing strategies to measure and improve system performance.
• Conduct root cause analysis, model interpretability reviews, and exploratory data analysis to drive accuracy.
• Partner with ML engineers to support model deployment, serving, versioning, and production pipeline best practices.
• Build customer analytics pipelines, reporting dashboards, and documentation for internal knowledge sharing.

Required Skills

• 5+ years of experience building deep learning models for large-scale recommender systems.
• Proficiency in Python, SQL, and Spark for data analysis and manipulation.
• Experience with ML frameworks such as TensorFlow or PyTorch.
• Knowledge of cloud platforms like Azure or GCP.
• Strong background in statistics, design of experiments, and insights generation.
• Ability to own toolkits, pipelines, and dashboards with high independence.
• Excellent problem-solving, analytical, and critical thinking skills.

Preferred Skills

• Experience working with Databricks.
• Experience in Data Engineering and MLOps.
• Prior experience in the retail or e-commerce industry.

Key Skills

Education

Any Gradute