✦ Luna Orbit — Data & Analytics

Senior Data Scientist, Search and Recommendations

at CarMax

📍 3 Locations Hybrid Posted March 13, 2026
Type Full-Time
Experience mid
Exp. Years 3+ years
Education Not specified
Category Data & Analytics

This role focuses on developing and maintaining search and recommender systems, analyzing behavioral data, and deploying ML models to improve customer experience.

  • Develop ML models
  • Optimize search/recommendation algorithms
  • Conduct statistical tests
  • Collaborate with ML engineers
  • Deploy models into production

The technical environment includes Python, SQL, deep learning frameworks, and statistical testing methods, with a focus on search algorithms and recommender system deployment.

The ideal candidate is a mid-level data scientist with 3+ years of experience in search and recommender systems, proficient in Python and SQL, with a strong understanding of ML models, deep learning, and statistical testing.

Data ScienceML ModelsSearch AlgorithmsRecommender SystemsPythonSQL
Deep LearningEmbedding ModelsRanking AlgorithmsSegmentation AlgorithmsA/B TestingStatistical TestingModel DeploymentML Lifecycle
PythonSQL
Data ScienceML ModelsSearch AlgorithmsRecommender SystemsDeep LearningEmbedding ModelsRanking AlgorithmsA/B TestingStatistical TestingPythonSQL
Data ScienceML ModelsSearch AlgorithmsRecommender SystemsDeep LearningEmbedding ModelsRanking AlgorithmsSegmentation AlgorithmsA/B TestingStatistical TestingPythonSQLModel DeploymentML Lifecycle
Analytical ThinkingCommunicationCollaborationProblem SolvingData-driven Decision Making
Industry Retail / E-commerce / SaaS
Job Function Develop and maintain search and recommendation ML systems
Senior Data ScientistSearchRecommender SystemsML ModelsDeep LearningEmbedding ModelsRanking AlgorithmsA/B TestingStatistical TestingModel DeploymentML LifecyclePythonSQLData ScienceSearch Algorithms

Lack of experience with search or recommender systems, No proficiency in Python or SQL, Less than 3 years of relevant experience, No experience with ML deployment or lifecycle

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