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About This Role
As a Data Scientist on the Behavior Evaluation team, you will be the statistical anchor ensuring our autonomous driving systems navigate highway environments with world\-class safety, efficiency, and comfort. Highway evaluation presents a unique industry challenge: verifying vehicle behavior at high velocities where the margin for error is razor\-thin, and critical edge cases are buried in petabytes of data.
In this role, you will bridge advanced statistical methodology with scalable software engineering. You will design the mathematical frameworks, statistical tests, and data\-driven metrics that evaluate our planner's decisions. Working directly with large\-scale simulation and real\-world fleet data, your insights will define our validation pipelines, identify behavioral regressions, and directly shape the software powering our next\-generation autonomous fleet.
### In this role, you will:
- Design Advanced Experimental Frameworks: Formulate robust statistical models, hypothesis testing frameworks, and quasi\-experimental designs (such as synthetic controls or matching) to rigorously validate highway planner behavior in simulation and shadow\-mode deployments.
- Model Tail Risks \& Rare Events: Use Surrogate Safety Measures (e.g., TTC, PET) to accurately model and predict low\-frequency, high\-severity edge cases that traditional mean\-based statistics miss.
- Architect Scenario\-Based Metrics: Own and mature critical behavioral KPIs, utilizing data stratification to analyze complex driving scenarios (e.g., high\-speed merging, cut\-ins) while proactively identifying statistical anomalies like Simpson’s Paradox.
- Surface Statistical Edge Cases: Apply data mining and advanced statistical techniques to isolate low\-frequency, high\-severity edge cases and systemic Autonomy engineering debt.
- Drive Cross\-Functional Alignment:Translate complex statistical findings and multi\-source evaluations into clear, actionable technical recommendations, collaborating closely with Autonomy Software Engineers, Safety Systems, and Product teams.
### Qualifications:
- Education: Bachelor’s or Master’s degree in a highly quantitative field (e.g., Statistics, Mathematics, Data Science, Operations Research, or a related field with a strong statistical focus).
- Experience: 3–6\+ years of professional experience as a Data Scientist or Quantitative Engineer, with a proven track record of landing data\-driven impact.
- Strong Statistical Foundations: Deep understanding of hypothesis testing, experimental design, regression analysis, non\-parametric/resampling methods (e.g., bootstrapping, permutation tests), and time\-series analysis handling autocorrelated data.
- Strong Programming: High proficiency in Python (Pandas, NumPy, SciPy, scikit\-learn) and the ability to write highly complex, optimized SQL queries for massive distributed databases.
- Communication: Exceptional ability to articulate complex mathematical methodologies and statistical results to cross\-functional engineering partners.
### Bonus Qualifications
- Robotics or Autonomy Background: Experience analyzing spatial\-temporal data, sensor logs, or vehicle telemetry from robotics, autonomous vehicles, or aviation systems.
- Simulation\-Based Testing: Familiarity with validating software systems using empty\-world or simulation platforms at scale.
- Modern Data Stack: Experience with workflow orchestration tools (e.g., Airflow) and building advanced data visualization layers (e.g., Superset).
Base Salary Range
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign\-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long\-term care insurance, long\-term and short\-term disability insurance, and life insurance.
About Zoox
Zoox is developing the first ground\-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility\-as\-a\-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast\-moving and highly execution\-oriented team.
Follow us on LinkedIn Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter. *A Final Note:**You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $176K-$240K range is above the 75th percentile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Zoox, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($208K) sits 5% above the category median. Disclosed range: $176K to $240K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Zoox AI Hiring
Zoox has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Boston, MA, US, Foster City, CA, US. Compensation range: $240K - $290K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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