Overview

ABOUT THE COMPANY

Enterprise Mobility is a leading provider of mobility solutions, owning and operating the Enterprise Rent-A-Car, National Car Rental and Alamo Rent A Car brands through its integrated global network of independent regional subsidiaries. Enterprise Mobility and its affiliates offer extensive car rental, carsharing, truck rental, fleet management, retail car sales, as well as travel management and other transportation services, to make travel easier and more convenient for customers.

Privately held by the Taylor family of St. Louis, Enterprise Mobility together with its affiliate Enterprise Fleet Management manages a diverse fleet of 2.4 million vehicles and accounted for nearly $39 billion in revenue through a network of more than 9,500 fully-staffed neighborhood and airport rental locations in more than 90 countries and territories.

As we continue to build a team that drives us forward, we are excited to announce the opening for a Data Scientist.

About

ABOUT THE ROLE

The Data Scientist is a key driver of innovation, transforming data into actionable insights that improve business processes. In this role, you’ll develop cutting-edge analytical products—creating algorithms for automation, building predictive models, designing experiments, and applying causal inference techniques to observational data. You’ll also harness mathematical optimization to identify the most profitable business strategies. Success in this position requires strong collaboration with both technical and non-technical teams to ensure the creation, delivery, and adoption of impactful analytical solutions

This position is located at our Corporate Headquarters in Clayton, MO. This position is eligible for a hybrid work schedule.

Responsibilities

As a Data Scientist focused on revenue management, you will design and deploy advanced deep learning models to forecast demand. These models will enable branch-level decision-making, helping maximize revenue by leveraging historical trends and predictive analytics. In this role, you will collaborate closely with cross-functional teams to develop and implement analytical solutions that drive measurable business impact.

Collaborate with the team to design and deliver analytical solutions that drive business impact
Extract, clean, and manipulate structured and unstructured data from multiple sources
Perform exploratory data analysis to identify patterns, trends, and insights
Develop predictive models to support data-driven decision-making
Design and oversee experiments, ensuring accurate execution and interpretation of results
Apply causal inference techniques using observational data to uncover relationships
Prepare and deliver clear documentation of methodologies, findings, and recommendations
Create and present insightful reports and presentations for technical and non-technical audiences
Partner with cross-functional teams to implement and operationalize analytical solutions

Equal Opportunity Employer/Disability/Veterans

Qualifications

Required:

Must be presently authorized to work in the U.S. without a requirement for work authorization sponsorship by our company for this position now or in the future
Must reside in St. Louis, Missouri, or immediate surrounding area
Must have a Master’s Degree in a Statistical or Mathematical field (e.g. Engineering, Social Science, or Statistics)
Must have two (2+) years of experience with predictive models, statistical inference and deep learning
Must have experience using libraries like tensorflow or pytorch
Must have experience preparing and giving presentations to technical and non-technical audiences
Must have proficiency in R or Python
Must be committed to incorporating security into all decisions and daily job responsibilities

Preferred

Doctorate Degree in a Statistical or Mathematical field (e.g. Engineering, Social Science, or Statistics)
Experience designing experiments
Experience exploring and visualizing data
Experience using Linux/Unix
Experience using SQL
Experience working with data (merging, recording, etc.) from a variety of sources/formats
Experience working with observational data to attempt causal inference (e.g. matching, weighting, etc.)

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