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About This Role
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.
- Metrics & Experimentation: Define success and performance metrics for current and new business initiatives, with particular focus on A/B tests.
- Data Engineering: Design and maintain robust ETL processes that keep data accurate and accessible, and oversee the upkeep and optimization of existing pipelines and dashboards.
- Reporting & Visualization: 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.
- Stakeholder Partnership: 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.
What We're Looking For:
- Education: Bachelor's degree or higher in Computer Science, or another quantitative field such as statistics, mathematics, physics, or engineering.
- Experience:
- 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.
- Technical Skills:
- ML & Modeling: Strong understanding of supervised and unsupervised learning, feature engineering, model selection, statistical evaluation, and the diagnosis of training, inference, and data-quality issues.
- Python & Frameworks: Strong Python skills and practical experience with common data science and ML libraries, such as pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
- AI-Assisted Development:
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 LLMs, prompting, or agentic/GenAI workflows is a plus.
- Data Pipelines & Platforms: Experience with scalable data pipelines, cloud data platforms, and production or near-production analytical workflows.
- SQL & Visualization: Strong SQL skills with structured and semi-structured data, and experience with data visualization tools such as Tableau.
- Curiosity & Craft: A demonstrated interest in learning, testing, and responsibly applying emerging AI and data science capabilities.
- Problem-Solving: A proven record of identifying and diagnosing problems and solving complex ones with simple, logical solutions.
- Communication: Outstanding verbal, written, presentation, and facilitation skills, with the ability to explain technical and non-technical concepts across organization levels.
- Collaboration: Strong interpersonal skills and a track record of building relationships across teams.
- Adaptability & Agility: Comfortable operating in a fast-paced environment, managing ambiguity, and adjusting to changing priorities and technologies.
What You’ll Get:
- Up to 40% off the base rate of any standard Hertz Rental
- Paid Time Off
- Medical, Dental & Vision plan options
- Retirement programs, including 401(k) employer matching
- Paid Parental Leave & Adoption Assistance
- Employee Assistance Program for employees & family
- Educational Reimbursement & Discounts
- Voluntary Insurance Programs - Pet, Legal/Identity Theft, Critical Illness
- Perks & Discounts –Theme Park Tickets, Gym Discounts & more
The Hertz Corporation operates the Hertz, Dollar Car Rental, Thrifty Car Rental brands in approximately 9,700 corporate and franchisee locations throughout North America, Europe, The Caribbean, Latin America, Africa, the Middle East, Asia, Australia and New Zealand. The Hertz Corporation is one of the largest worldwide airport general use vehicle rental companies, and the Hertz brand is one of the most recognized in the world. US EEO STATEMENTAt Hertz, we champion and celebrate a culture of diversity and inclusion. We take affirmative steps to promote employment and advancement opportunities. The endless variety of perspectives, experiences, skills and talents that our employees invest in their work every day represent a significant part of our culture – and our success and reputation as a company. Individuals are encouraged to apply for positions because of the characteristics that make them unique. EOE, including disability/veteran
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Hertz, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Hertz AI Hiring
Hertz has 2 open AI roles right now. They're hiring across Data Scientist. Based in Estero, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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