Data Scientist, Production Analytics - Tempe, AZ
1 month ago
Job summary
About Carvana At Carvana we're changing the way people buy and sell cars With an ambitious vision and a fundamentally different approach designed to be fun fast and fair Carvana became the fastest-growing automotive retailer in history We expanded nationally went public on the New York Stock Exchange sold our 1 millionth car and reached the Fortune 500 all in just eight years Today with 4 million retail customers and counting Carvana is both the fastest-growing and most profitable public automotive retailer we're just getting started We continue to raise the bar for our customers as we tackle enormous opportunity still ahead in largest consumer vertical Working here means being part of team that embraces change celebrates creative problem solving always strives to better At Carvana you'll have opportunity take on meaningful challenges learn quickly help shape future automotive retail If driven grow make impact collaborative team fit right in Learn more what it's like work here people already do
Responsibilities
- Building maintaining predictive models Developing neural network models primarily TensorFlow/Keras that leverage vehicle characteristics operational data Building models support operational decision-making performance insights Maintaining improving existing production models business needs evolve Using LightGBM linear/logistic regression benchmarking explanatory analysis Implementing hyperparameter optimization model tuning strategies Monitoring model performance identify recalibration needed Performing rigorous exploratory data analysis Investigating data quality training models validating matches operational reality Ground expectations actual happening business not just what data says Identifying anomalies edge cases data quality issues impact model performance Creating visualizations communicate patterns insights stakeholders Documenting validation findings decisions Deploying monitoring production models Working engineering teams integrate into systems Tracking prediction accuracy drift time Developing validation frameworks ensure perform expected Iterating based production performance changing business needs Collaborating communicating Translating problems modeling approaches Explaining predictions limitations non-technical stakeholders Partnering operations understand domain context Documenting architecture assumptions
Job description
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