Data Scientist

Data ScientistEnterprise 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.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 offers the opportunity to work fully remote within the United States (except for Alaska and/or Hawaii). Team members who choose virtual / remote work should have an adequate space to serve as their home office, and must be able to work a schedule within U.S. Central Standard Time core business hours. This position will require employees to come on site to one of our St. Louis campus locations a few times per year for meetings/events or as needed. #LI-REMOTEAs 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 impactExtract, clean, and manipulate structured and unstructured data from multiple sourcesPerform exploratory data analysis to identify patterns, trends, and insightsDevelop predictive models to support data-driven decision-makingDesign and oversee experiments, ensuring accurate execution and interpretation of resultsApply causal inference techniques using observational data to uncover relationshipsPrepare and deliver clear documentation of methodologies, findings, and recommendationsCreate and present insightful reports and presentations for technical and non-technical audiencesPartner with cross-functional teams to implement and operationalize analytical solutionsEqual Opportunity Employer/Disability/VeteransRequired: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 futureMust reside in the United States (does not include Alaska or Hawaii)Must be able to work a schedule within U.S. Central Standard Time core business hours.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 learningMust have experience using libraries like tensorflow or pytorchMust have experience preparing and giving presentations to technical and non-technical audiencesMust have proficiency in R or PythonMust be committed to incorporating security into all decisions and daily job responsibilitiesPreferred:Doctorate Degree in a Statistical or Mathematical field (e.g. Engineering, Social Science, or Statistics)Experience designing experimentsExperience exploring and visualizing dataExperience using Linux/UnixExperience using SQLExperience working with data (merging, recording, etc.) from a variety of sources/formatsExperience working with observational data to attempt causal inference (e.g. matching, weighting, etc.)