Interested in this Data Scientist role at Joby Aviation?
Apply Now →Skills & Technologies
About This Role
Company Overview
Joby Advanced Development designs, develops, and flight-tests novel aircraft using a software-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen-electric aircraft in both CTOL and VTOL configurations. Overview
Joby Advanced Development is seeking a Autonomy Senior Data Scientist, to build the AI data and evaluation platform for our Superpilot™ autonomy stack. This position offers a unique opportunity to design and build the AI data science solutions that power Joby’s varied fleet of autonomous aircraft. In this role, you will develop data-centric AI solutions to autonomy problems, involving data engineering and machine learning. The role requires broad industry experience and deep technical knowledge of data science and data engineering.
You will build robust software for flight telemetry, sensor, and imagery data, turning raw logs, collected data, and synthetic data into reliable AI datasets, metrics, and workflows that support development, validation, and deployment. Further, you will also build AI solutions using this data to solve autonomy problems in perception and other applications. Your work will help teams develop algorithms, detect regressions, investigate edge cases, and validate safety-critical behavior long before it reaches flight operations.
We are a small, high-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with perception, controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong AI data scientist who cares deeply about system design, reliability, developer experience, and enabling fast, safe iteration across multiple aircraft programs. Responsibilities
- AI Data Annotation and Curation + Design, implement, and maintain a scalable auto-labeling pipeline for a heterogeneous fleet of aircraft and simulators and their associated imagery and data + Design, implement, and maintain a scalable data quality pipeline for curating, filtering, and analyzing data for autonomy data + Develop data-centric algorithms and software to catalog the data (metadata, anomaly detection, missing data)
- AI Data Evaluation and Analysis + Build the AI data software that transforms raw flight and simulation data into trustworthy metrics, curated datasets, and actionable engineering insight + Develop scalable pipelines for AI algorithm evaluation, analysis, and visualization
- AI Solutions Development + Design and develop data-centric AI solutions for autonomy problems in perception and decision-making + Proactively explore the data, develop new metrics, interpret trends, and investigate anomalies in data from both real world and simulation
- Collaborate closely with the rest of the Superpilot™ team to ideate, plan and execute on high-quality, well-integrated solutions and feature
- Monitor system health and performance, proactively addressing issues and providing user support
Required
- Degree in computer science, aerospace engineering, or related field
- 8+ years of experience in data science
- Expertise in machine learning model development, evaluation, and deployment
- Expertise in data analytics, dashboards, and visualization
- Experience in building dataset management solutions, platforms, and infrastructure
- Strong proficiency in Python and Git within a Linux-based environment
- Experience with cloud data and compute (AWS, GCP, Azure)
- Proven ability to document complex technical designs, architectural trade-offs, and implementation roadmaps
- Champion of software best practices, including rigorous code reviews and mentorship
- Excellent communication skills for collaborating with cross-functional teams
This position must meet US export control compliance requirements, therefore a candidate must qualify as a “US Person” as defined by 22 C.F.R. § 120.15. “US Person” includes US Citizens, lawful permanent residents, refugees, or asylees. Desired
- Expert-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code
- Experience data engineering infrastructure, platforms, tools
- Experience processing high-bandwidth sensor data from robotics such as cameras, lidar, radar
- Experience in processing aircraft data such as GPS, inertial, air data, radio data
- Experience with MLOps and DevOps to deploy ML models in a production environment using tools such as MLflow, Dagster, Docker, Kubernetes
Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $149,900 - $202,600/yr. The compensation package will be determined by job-related knowledge, skills, and experience.
Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short-term and long-term disability coverage, life insurance, and more. Additional Information
Joby Aviation is an equal opportunity employer.
Salary Context
This $149K-$202K range is above the median for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Joby Aviation, 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 $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($176K) sits 7% below the category median. Disclosed range: $149K to $202K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Joby Aviation AI Hiring
Joby Aviation has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Santa Cruz, CA, US. Compensation range: $202K - $250K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.