Data Scientist

A Day in the Life:

The Senior Data Scientist, Fleet Planning builds and ships the analytics, data science, and ML systems behind Hertz's fleet — from predictive and prescriptive models to the simulations, experiments, data pipelines, and dashboards that turn them into decisions and measurable business impact. You'll operate as a hands-on individual contributor, working closely with data engineers, analysts, software developers, and product managers to take that work from idea to production.

This role calls for strong technical grounding across data science, ML, and experimentation methods, including how to debug and validate them, and a bias toward using AI-assisted development workflows and emerging generative AI capabilities to accelerate delivery. You'll dig into what shapes fleet and product performance, partner with stakeholders across the business to turn that into decisions they can act on, and flag risks and opportunities early enough for leaders to act on them.
 

The starting salary for this role is $110K, commensurate with experience.

What You'll Do:
 

• Problem Solving & Modeling: Use analytical and machine learning methods to dissect complex business problems and build descriptive, predictive, and prescriptive models that turn data into actionable insight and drive measurable impact for new products and services.

• Solution Delivery: Own the end-to-end data science and ML lifecycle, from problem framing and feature engineering through experimentation, offline evaluation, production deployment, monitoring, and iteration based on real-world performance.

• Simulation & Decisioning: Build simulation environments used to train and evaluate sequential decision-making methods, including reinforcement learning where appropriate, with attention to simulator fidelity, reward design, and offline/online evaluation.

• AI-Assisted Development: Use approved AI-assisted development tools (e.g., Claude Code, Cursor, GitHub Copilot, Databricks Genie) to accelerate your own analysis, coding, testing, and documentation. Critically review, test, and harden AI-generated code and outputs for accuracy, security, reproducibility, and compliance with organizational standards.

• Applied GenAI: Explore and prototype generative AI and agentic AI solutions where they fit the business problem: retrieval-augmented generation, structured data interaction, tool-using agents, workflow automation, decision support, and natural-language interfaces. Put evaluation, guardrails, security controls, and human oversight in place before any production use.