Senior Data Scientist

Seeking a Senior Data Scientist to join a high-impact Personalization & Loyalty Strategy team supporting one of the largest e-commerce organizations in the United States. This team powers trillions of recommendation decisions annually and delivers highly personalized experiences to millions of customers.

This role is focused on designing and building next-generation recommender systems, personalization engines, and deep learning models that influence product discovery, coupon recommendations, substitute recommendations, and shoppable recipe experiences.

The ideal candidate brings hands-on experience developing large-scale recommendation systems, deep learning expertise, and a passion for turning customer behavior data into meaningful business outcomes.

• Recommender Systems / Personalization Experience

• Deep Learning Model Development

• TensorFlow or PyTorch

• Python

• SQL

• Apache Spark

• Machine Learning Model Evaluation

• Experiment Design / A-B Testing

• Statistical Analysis

• Customer Personalization

• 2+ years of experience building large-scale recommender systems.

• Experience developing deep learning models for personalization use cases.

• Strong proficiency with TensorFlow or PyTorch.

• Strong programming skills in Python.

• Advanced SQL proficiency.

• Experience using Apache Spark for large-scale data processing.

• Strong understanding of:

  • Statistics

  • Experimental Design

  • Hypothesis Testing

  • Exploratory Data Analysis

  • Machine Learning Evaluation Metrics

• Experience working in cloud environments such as Azure or GCP.

• Strong communication and presentation skills.

• Ability to work independently and take ownership of initiatives.

• Excellent analytical and problem-solving skills.

• Experience with Databricks.

• Experience supporting production ML systems.

• MLOps experience.

• Data Engineering experience.

• Retail, grocery, loyalty, or e-commerce experience.

• Search relevance and ranking experience.

• Experience working with large-scale customer behavior datasets.

• Databricks

• Azure or GCP

• MLOps

• Data Engineering

• Retail / E-Commerce Experience

• Search Relevancy Systems

• Customer Analytics

As a member of the Relevancy Team, you will build and optimize recommendation engines that improve customer engagement and drive revenue growth through personalized experiences.

You will work alongside data scientists, machine learning engineers, software engineers, data engineers, product managers, and business stakeholders to design, train, evaluate, deploy, and continuously improve recommendation systems operating at enterprise scale.

This role offers the opportunity to solve complex machine learning challenges involving customer behavior, product affinity, loyalty engagement, and personalization strategies.

• Design, build, and optimize recommendation engines for e-commerce personalization.

• Develop deep learning models for product recommendations, coupon recommendations, substitute recommendations, and recipe recommendations.

• Research and implement advanced recommendation algorithms including:

  • Collaborative Filtering

  • Matrix Factorization

  • Deep Learning Recommenders

  • Sequence Models

  • Embedding-Based Approaches

  • Hybrid Recommendation Systems

• Define evaluation frameworks and success metrics.

• Perform offline model evaluation and online experimentation.

• Conduct A/B testing to compare recommendation strategies.

• Analyze recommendation quality, diversity, and customer engagement metrics.

• Perform root cause analysis to improve recommendation accuracy and relevance.

• Incorporate customer preferences, shopping behavior, engagement history, and loyalty data into recommendation models.

• Improve personalization experiences using transactional, demographic, behavioral, and product data.

• Develop strategies that balance recommendation relevance with recommendation diversity.

• Partner with ML Engineers to support:

  • Model deployment

  • Model serving

  • Model monitoring

  • Model versioning

  • Production pipelines

• Contribute to MLOps and operationalization best practices.

• Build customer analytics datasets and performance dashboards.

• Develop reporting solutions to monitor recommendation effectiveness.

• Generate actionable insights for business stakeholders.

• Collaborate closely with Data Science, Engineering, Product, and Business teams.

• Document technical approaches, findings, and best practices.

• Contribute reusable tools, libraries, and internal frameworks.

• Participate in technical mentoring and knowledge-sharing sessions.

• Python

• SQL

• Apache Spark

• TensorFlow

• PyTorch

• Databricks

• Azure

• GCP

• Machine Learning

• Deep Learning

• Recommender Systems

• Personalization Engines

• A/B Testing

• MLOps