Senior Data Scientist - Fleet Analytics

Hertz•West Bay Club, FL

About The Position

Hertz is one of the largest global mobility organizations and is currently undergoing a significant digital transformation. Hertz is committed to becoming an essential component of the modern mobility ecosystem which includes leading in digital-first customer experience, shared mobility and electrification. At Hertz, we are at the forefront of innovation, leveraging cutting-edge technologies to build the best damage management technologies in the transportation industry. We want the brightest AI & ML minds to join our Damage Science Team to help us develop and/or maintain capabilities. Examples of projects the team work on include the below: Rationalize Repair Estimates and Invoices with Large Language Models (LLMs): Implement sophisticated LLMs to make intelligent repair routing decisions, ensuring repairs are conducted efficiently and cost-effectively. Forecast Repair Needs: Develop models to predict future repair & maintenance needs based on historical data and trends. Optimize Decision Making: Create models to determine if we should keep/sell/salvage a vehicle. Demand Planning: Forecast customer demand at a given location at a given time.

Requirements

3-5 years hands-on experience in a data scientist role
3+ years of data querying languages (e.g. SQL) and scripting languages (e.g. Python)
3+ years of end-to-end machine learning model development experience (e.g. problem statement definition, exploratory data analysis, feature engineering, model development / tuning, and deployment)
Demonstrated experience using machine learning to drive a business impact
Experience in a ML or data scientist role with car rental or technology company.
Bachelor or Masters degree in Computer Science, or other quantitative discipline such as statistics, mathematics, physics or engineering

Responsibilities

Formulate the strategic and tactical steps to carry out the model development lifecycle end-to-end (i.e. problem statement definition, exploratory data analysis, feature engineering, model development / tuning, and model implementation).
Build and maintain descriptive, predictive, and prescriptive models to measure the performance of the new products and services
Define and implement best practices to generate accurate analytics, reports, visualizations, and dashboards to explain results simply and succinctly to technical, non-technical, and senior management
Build partnerships and work cross-functionally to identify use cases and opportunities to enhance operational efficiency and drive business value through positive impact on OKRs.
Work with an owner mentality to drive business impact even if that means supporting pipeline creation or decision support analytics.