Sr Data Scientist
Hertz Global Hldgs INC - Atlanta, GA, United States
Posted Apr 14, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Sr Data Scientist Atlanta, GA, United States A day in the life: At Hertz, our senior data scientists are responsible developing and defining best practices for analytical methods to generate informative research, reports, visualizations, and dashboards using internal and external data to provide actionable and strategic insights. You will work closely with data engineers, analysts, software developers and product managers within the business to deliver new and exciting products and services. Your main objective is to leverage advanced analytics and machine learning techniques to deeply understand our product performance and generate insights to drive operational efficiency and effectiveness by enabling proactive data-driven decisions across the organization. This includes fostering collaboration, building partnerships, co-developing products, sharing knowledge, providing insights and valuable predictive information to business teams and leaders to highlight potential risks and opportunities that initiate the drive for change. The role of the incumbent is to work on impactful strategic initiatives and use state of the art technologies to solve complex problems to enable and optimize our digital, connected fleet, pricing, and ridesharing products. The starting salary for this role is $105K; commensurate with experience. What You'll Do: Dissect and solve complex business problems by applying analytical and machine learning techniques to uncover patterns and correlations in large, complex datasets Own end-to-end ML model development: problem framing, data architecture scoping, feature engineering, model design, experimentation, evaluation, deployment, monitoring Analyze and define success and performance metrics for current and new business initiatives, with a specific focus on A/B tests Design and
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Apply link verified; last checked Jun 13, 2026.
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