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

Education

Any Gradute