Description

Requirements

• 2+ years of proven experience building deep learning models for large-scale recommender systems. 
• Proficiency in ML frameworks such as TensorFlow or PyTorch. 
• Proficiency in SQL, Python and Spark for data analysis and manipulation. Experience working with Databricks is a plus. 
• Proficiency with statistics, design of experiments, exploratory data analysis, and insights generation. 
• Experience working with cloud platforms like Azure or GCP. 
• Experience working with Data Engineering and MLOps is desirable. 
• High level of independence to develop and own toolkits, pipelines, and dashboards. 
• Excellent problem-solving skills and a proactive approach to addressing challenges. 
• Strong analytical and critical thinking skills with attention to detail. 
• Prior experience in the retail or e-commerce industry is a plus.  
• Must be able to learn from others and teach others and work collaboratively as part of a highly interdependent team. 
• Ability to communicate complex ideas effectively to both technical and non-technical stakeholders.

Key Responsibilities

• Design, develop, and implement recommender systems tailored to grocery retail and e-commerce personalization needs.
• Build advanced machine learning and deep learning models to deliver personalized product, coupon, substitute, and recipe recommendations.
• Define evaluation methods and key metrics to measure recommender system performance and identify areas for improvement.
• Conduct A/B testing and offline model evaluations to compare recommendation strategies and improve model outcomes.
• Perform root cause analysis and model interpretability reviews to understand recommendation results and improve accuracy.
• Improve personalization by incorporating customer preferences, dietary needs, shopping behaviors, and engagement patterns.
• Explore recommendation diversity strategies that expose customers to a broader range of relevant products while maintaining accuracy.
• Partner with ML engineers to support model deployment, serving, versioning, and production pipeline best practices.
• Collaborate with data scientists, data engineers, full stack engineers, product teams, and business stakeholders to deliver data science solutions.
• Integrate transactional, customer, product, demographic, and user feedback data to support model development and analytics.
• Build customer analytics pipelines, reporting dashboards, and performance tracking to monitor recommendation effectiveness over time.
• Document best practices, technical insights, lessons learned, and model development approaches for internal knowledge sharing.
• Contribute to internal tools, libraries, and documentation that support adoption and maintenance of recommender system solutions.
• Participate in knowledge-sharing sessions and technical discussions to support continuous learning across the team

Key Skills

Education

Any Gradute