Interested in this Data Scientist role at Hertz?
Apply Now →Skills & Technologies
About This Role
*Hertz Revenue Management is seeking a highly motivated, intellectually curious, and talented Sr. Data Scientist to join a team that is responsible for optimizing Hertz's revenue offers. The Sr. Data Scientist will collaborate on designing, delivering, and maintaining proprietary optimization products that result in improved customer offers and increased revenue for Hertz. The role partners across Revenue Management, Pricing, Sales, Product, Distribution, Operations Research, IT, and vendors to identify opportunities, define requirements, provide insights, prototype products, and deliver end\-to\-end solutions*
What You’ll Do:* Identify commercial opportunities to optimize Hertz revenue that can be addressed by developing data sources, methods, tools and models, including time series forecasting, reinforcement learning, or optimization modeling
- Enhance existing analytical tools and develop new solutions/tools based on large data sets
- Own and deliver end\-to\-end solutions, including coordination with stakeholders and IT, implementation, and support
- Promote the long\-term customer vision that supports Hertz's business strategy as the marketplace evolves, and new products are introduced
- Actively develop close relationships and coordinate with commercial business units, Operations Research, Finance, IT and senior management to ensure timely execution of Hertz Revenue Management sponsored projects
- Maintain a pulse on industry efforts, disruptive technologies, and champion solutions to further Hertz's position as industry leader
- Balance multiple priorities in a fast\-paced environment
- Understand the interrelationships between Fleet, Pricing, and Revenue Management, and the combined impact on the Hertz Customer experience
- Developing the Data Analytics Team
- Other duties and tasks as assigned
What We’re Looking For:
- Master’s degree in operations research, statistics, computer science, or a related quantitative field required.
- 2\-5 years of industry experience in modeling, data science or operations research required.
- Highly proficient communication skills and ability to actively advocate for Hertz Revenue Management solutions across the enterprise
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/optimization software (e.g. R, SAS, Gurobi, C\+\+, etc.) required.
- Experience with big data: processing, filtering, and presenting large quantities of data required.
- Ability to understand and work with large, unstructured data sets
- Proven success in solving complex quantitative problems
- Must be able to work well with diverse groups in a time\-sensitive team environment with minimal supervision, be results oriented and able to meet or exceed deadlines with attention to detail and follow through
- Demonstrated ability to handle multiple projects through to solution delivery
- Consistently prioritizes safety and security of self, others, and personal data.
- Embraces diverse people, thinking, and styles.
Preferred Qualifications:
- 5\+ years in operations research, statistics, computer science, or related science field.
- Experience with time series forecasting, reinforcement learning, machine learning, or optimization.
- Knowledge of revenue management theory or travel industry systems/data
*The starting salary for this role is $105K; commensurate with experience.*
What You’ll Get:
- 40% off 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Hertz AI Hiring
Hertz has 1 open AI role right now. They're hiring across Data Scientist. Based in GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.