Sr Data Scientist - Fleet Planning

Hertz•Estero, FL

About The Position

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.

Requirements

Bachelor's degree or higher in Computer Science, or another quantitative field such as statistics, mathematics, physics, or engineering.
5+ years of hands-on experience in a data science or data analytics role.
2+ years of intensive experience with cloud-based Big Data, ML, and analytics technologies, preferably Databricks.
A demonstrated record of delivering data science or ML solutions across multiple lifecycle stages: problem definition, data preparation, modeling, evaluation, implementation, and post-deployment performance assessment.
Strong understanding of supervised and unsupervised learning, feature engineering, model selection, statistical evaluation, and the diagnosis of training, inference, and data-quality issues.
Strong Python skills and practical experience with common data science and ML libraries, such as pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
Practical experience using AI-assisted development tools (e.g., Claude Code, Cursor, GitHub Copilot, Databricks Genie) to accelerate coding, analysis, and documentation, plus the judgment to critically review, test, and harden AI-generated outputs.
Experience with scalable data pipelines, cloud data platforms, and production or near-production analytical workflows.
Strong SQL skills with structured and semi-structured data, and experience with data visualization tools such as Tableau.
A demonstrated interest in learning, testing, and responsibly applying emerging AI and data science capabilities.
A proven record of identifying and diagnosing problems and solving complex ones with simple, logical solutions.
Outstanding verbal, written, presentation, and facilitation skills, with the ability to explain technical and non-technical concepts across organization levels.
Strong interpersonal skills and a track record of building relationships across teams.
Comfortable operating in a fast-paced environment, managing ambiguity, and adjusting to changing priorities and technologies.

Nice To Haves

Experience with LLMs, prompting, or agentic/GenAI workflows is a plus.

Responsibilities

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.
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.
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.
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.
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.
Define success and performance metrics for current and new business initiatives, with particular focus on A/B tests.
Design and maintain robust ETL processes that keep data accurate and accessible, and oversee the upkeep and optimization of existing pipelines and dashboards.
Integrate and curate data from internal and external sources, and set best practices for analytics, reports, visualizations, and dashboards that explain results clearly to technical, non-technical, and senior audiences.
Work with stakeholders across the company to understand their needs and turn that understanding into analytics that shape strategy and performance. Guide business leaders with confidence in your analysis so execution stays smooth.