Sr Data Scientist - Fleet Planning

Hertz

Estero (FL)

On-site

USD 94,000 - 127,000

Full time

9 days ago

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Benefits offered by this job

PTO

Medical, Dental & Vision

401(k) matching

Parental Leave

Educational Reimbursement

Employee Assistance Program

Pet Insurance

Theme Park Discounts

Job summary

Hertz is seeking a Senior Data Scientist for Fleet Planning in Estero, FL. This hands-on role focuses on building analytics, ML/GenAI systems, and simulations to improve fleet planning and management.

You will own the end-to-end DS lifecycle, deploy models, and collaborate with stakeholders to drive measurable outcomes. The ideal candidate has 5+ years in DS/analytics, strong Python skills, and experience with AI-assisted tools.

Qualifications

Bachelor’s degree in CS or a quantitative field.
5+ years hands-on data science or analytics experience.
2+ years cloud-based data/ML tech, Databricks preferred.
Proficient in Python and ML libraries (pandas, NumPy, scikit-learn, PyTorch, TensorFlow).
Experience with AI-assisted development tools and production ML lifecycle.

Responsibilities

Apply ML methods to deliver descriptive, predictive, and prescriptive models.
Own end-to-end DS/ML lifecycle from problem framing to deployment and monitoring.
Build simulation environments for sequential decision-making and RL when appropriate.
Prototype AI solutions including retrieval-augmented and tool-using agents.
Define success metrics, ensure guardrails, and publish reproducible results.

Skills

5+ years data science

2+ years cloud-based big data

Python and ML libraries

SQL

AI-assisted tooling experience

Strong communication

Education

Bachelor’s degree in CS or quantitative field

Tools

Databricks Genie

Claude Code

Cursor

GitHub Copilot

Tableau

Job description

At Hertz, the Senior Data Scientist for Fleet Planning helps turn data into decisions that improve how the fleet is planned and managed. This is a hands-on individual contributor role where you build and ship analytics, data science, and ML/GenAI systems, moving from predictive and prescriptive modeling through simulations, experiments, pipelines, and dashboards for production impact.

What you will do

Apply analytical and machine learning methods to break down complex business challenges and deliver descriptive, predictive, and prescriptive models that produce measurable outcomes for new products and services.
Own the end-to-end data science and ML lifecycle, including problem framing, feature engineering, experimentation, offline evaluation, production deployment, monitoring, and iterative improvement based on real-world performance.
Build simulation environments to train and evaluate sequential decision-making approaches, including reinforcement learning when appropriate, with focus on simulator fidelity, reward design, and offline and online evaluation.
Accelerate analysis and development using approved AI-assisted coding tools such as Claude Code, Cursor, GitHub Copilot, and Databricks Genie.
Critically review, test, and harden AI-generated code and outputs to support accuracy, security, reproducibility, and organizational compliance.
Prototype generative AI and agentic AI solutions where they fit the business problem, including retrieval-augmented generation, structured data interaction, tool-using agents, workflow automation, decision support, and natural language interfaces.
Ensure evaluation, guardrails, security controls, and human oversight are in place before deploying any solution in production.
Define success and performance metrics for initiatives, with a particular focus on A/B tests.
Design and maintain robust ETL processes to keep data accurate and accessible, including upkeep and optimization of existing pipelines and dashboards.
Integrate and curate data from internal and external sources, and establish best practices for analytics, reports, visualizations, and dashboards for both technical and non-technical audiences.
Partner with stakeholders across the company to translate needs into analytics that shape strategy and performance.
Support business leaders by explaining analysis clearly so execution stays aligned.

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 data science or data analytics.
2+ years of intensive experience with cloud-based Big Data, ML, and analytics technologies, preferably Databricks.
Proven delivery across multiple ML lifecycle stages: problem definition, data preparation, modeling, evaluation, implementation, and post-deployment performance assessment.
Strong knowledge of supervised and unsupervised learning, feature engineering, model selection, statistical evaluation, and diagnosing issues across training, inference, and data quality.
Strong Python skills with experience using common ML libraries including pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
Practical experience with AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, Databricks Genie), plus the judgment to critically review and harden AI-generated outputs.
LLM, prompting, or agentic/GenAI workflow experience is a plus.
Experience with scalable data pipelines, cloud data platforms, and production or near-production analytical workflows.
Strong SQL skills for structured and semi-structured data, plus experience with visualization tools such as Tableau.
Demonstrated interest in learning, testing, and responsibly applying emerging AI and data science capabilities.
Track record of identifying and diagnosing problems and solving complex issues with clear, logical approaches.
Excellent verbal, written, presentation, and facilitation skills, including the ability to explain technical and non-technical concepts across levels.
Strong interpersonal skills and experience building relationships across teams.
Comfort working in a fast-paced environment with ambiguity and changing priorities.