Sr Data Scientist - Fleet Planning

Fa Evlf Saasfaprod1 Open · verified Oct 24, 2026

Estero Full-time Senior Data Science

At a glance

Senior Data Science role at Fa Evlf Saasfaprod1. Estero · full-time · $100,000–$125,000 base.

SalaryStated by Fa Evlf Saasfaprod1

$100,000 – $125,000

Base salary for this role, in USD per year, as published in the posting.

RoleData Science

SenioritySenior

LocationEstero

EmploymentFull-time

Base pay$100,000–$125,000

PostedOct 22, 2026 · 2d ago

Role brief

dataroles summary, based on the employer's posting.

What you'll do

Build descriptive, predictive and prescriptive models to turn fleet and product data into decisions with measurable results
Carry ML work from framing and feature design through testing, deployment, monitoring and updates based on real-world results
Create simulations to train and assess sequential decision methods, including reinforcement learning when useful
Use approved AI coding tools, then test and secure their outputs; prototype generative AI solutions with evaluation and safeguards
Set initiative metrics, especially for A/B tests, and maintain data pipelines, curated datasets, reports and dashboards

What you bring

A bachelor's degree or higher in computer science or a quantitative discipline such as statistics, mathematics or engineering
At least five years in hands-on data science or analytics, including two years with cloud-based big data, ML and analytics platforms
A record of delivering data science or ML work across multiple stages, from problem definition through post-launch assessment
Strong Python and SQL skills, plus experience with ML libraries, scalable data pipelines and production or near-production analytics
Practical use of AI-assisted development tools and the judgment to test and improve their generated code and analysis

Who this fits

This is a senior, hands-on individual-contributor role involving close work with data engineers, analysts, developers, product managers and business stakeholders. It suits someone who can explain technical findings to varied audiences and work through changing priorities.

From the employer

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 salary range for this role is $100,000 - $125,000, 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.