> Markdown version of [/jobs/ext/2710732-data-scientist](https://www.wearedevelopers.com/jobs/ext/2710732-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Scientist

- **Company:** Zoox
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Unit Testing, Big Data, Data Mining, Distributed Systems, Apache Hive, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Verification and Validation (Software), SQL Databases, Data Processing, Apache Spark, Safety Critical Systems, Git, Information Technology, Production Code, Power Analysis (Cryptography), Data Analytics, Machine Learning Operations, Databricks
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/senior-data-scientist-verification-validation-zoox-7889704

## About the Role

* MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field
 * Proficiency in Python and SQL with experience in production-quality code
 * Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.
 * Experience with large-scale data analysis and statistical modeling
 * Proficiency with Git, unit testing, and collaborative development practices, * Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines
 * Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks
 * Experience with designing metrics and delivering actionable insights that drive business decisions

## Description

You will join a team of software and data engineers that leverage methods including log data analysis, simulation, and closed-course structured testing. You'll work cross-functionally with AI software, System Design and Mission Assurance, Simulation, Sensors, and other teams to develop, execute, and iterate on validation methods and pipelines. These pipelines evaluate safety-critical systems, are highly visible, and are an important critical path element of launching our service. The ideal candidate brings a hybrid of statistical rigor and engineering mindset to drive clarity from ambiguity, establish new processes, and propel the team forward. This is a deeply technical and hands-on role where you will be expected to be a self-sufficient builder and coder, not just a manager of projects., * Design Evaluation Frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.

 * Conduct Robust Analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.
 * Inform Strategy: Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.
 * Define Metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.
 * Lead the Lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.
 * Scale Pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.


## Related Videos

- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)
- [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads)

## Related Articles

- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Dev Digest 166: Sycophancy, Zip bombs and AI Native Development](https://www.wearedevelopers.com/magazine/585-dev-digest-166-sycophancy-zip-bombs-and-ai-native-development)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)