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About This Role
A Day in the Life:As a Senior Data Scientist on the Data Science & Insights team, you will use data science to support business transformation by improving how decisions are made and how complex problems are solved. You will work on practical, scalable data science solutions that improve operational performance, strengthen forecasting, and enhance the customer experience. You will balance technical depth with business judgment, moving quickly from problem definition to usable solutions, collaborating across teams, and helping the organization make faster, smarter decisions.
The starting salary for this role is $115,000, commensurate with experience.
What You’ll Do:
- Lead the end-to-end model lifecycle, from problem framing and EDA to feature engineering, model tuning, deployment, and monitoring.
- Build scalable, reliable models and analytical solutions that support repeatable decision-making across products, services, and business processes.
- Apply these solutions to improve demand forecasting, fleet planning, vehicle turnaround, maintenance planning, and customer satisfaction.
- Create leadership-ready data stories that frame key metrics, explain the “why,” and guide business decisions.
- Partner with Product, Operations, Revenue Management, and Fleet teams to identify, prioritize, and deliver high-value use cases that support revenue growth, operating expense reduction, and improved business performance.
- Support the transformation by developing practical MVPs, testing quickly, and iterating solutions that unlock business value.
- Mentor junior data analysts and contribute to a culture of learning, innovation, and quick experimentation.
Stay current on data science methods, emerging technologies, and industry trends to continually improve team capabilities. *
What We’re Looking For:
- Education: Master’s degree preferred; Ph.D. in Data Science, Statistics, Computer Science, Mathematics, or a related field.
- Experience: 5+ years of experience in data science, with a proven track record of delivering impactful projects, preferably in the rental car, automotive, transportation, or hospitality industries.
- Technical Skills: + Strong proficiency in Python and SQL for data analysis, modeling, and automation. + 3+ years of end-to-end machine learning model development experience, from problem framing and EDA through feature engineering, tuning, and deployment. + Experience working with large datasets, databases, and distributed data processing tools such as PySpark. + Preferred hands-on experience with cloud-based data and analytics platforms such as Databricks, including building apps and dashboards. + Experience using LLMs to connect fragmented data sources, validate assumptions, and accelerate analysis is a plus.
- Analytical Skills:
+ Ability to connect the dots across data sources and shape analysis into practical solutions teams can implement.
- Communication:
+ Strong communication skills, with the ability to engage senior leaders and explain complex concepts clearly. + Ability to visualize metrics, analysis, and solutions in ways that drive understanding and action.
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 8,943 AI roles we're tracking, Data Scientist positions make up 4% 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 $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; 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% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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