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About This Role
Company Overview
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Imagine a piloted air taxi that takes off vertically, then quietly carries you and your fellow passengers over the congested city streets below, enabling you to spend more time with the people and places that matter most. At Joby, we've been working to make that dream a reality since 2009 and we're now in the final stages of certifying our aircraft with the FAA. With plans to launch our aircraft in the US and Dubai, we're now scaling manufacturing and preparing for the launch of our commercial service.
Overview
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Joby Aviation is seeking a Senior Data Scientist to join our Health and Usage Monitoring Systems (HUMS) team. In this role, you will be driving algorithmic development behind the predictive health, safety, and reliability of our aircraft. You will partner closely with multidisciplinary Subject Matter Experts (SMEs)—across Propulsion, Flight Test, Battery Systems, and Structures—to design, develop, and deploy advanced algorithms that monitor the health and usage of critical Joby subsystems. This is a senior individual\-contributor role for an engineer who thrives at the intersection of physical systems and modern data science. You will own your projects end\-to\-end: translating complex physical degradation phenomena into robust predictive models, and turning those models into production\-quality, well\-tested code. If you are passionate about blending signal processing, machine learning, and data\-engineering to shape the future of electric aviation, we want to talk to you. What we bring to the table is a truly unique data landscape. You will not analyze flight data in a vacuum. Instead, you will integrate high\-frequency flight sensor telemetry with comprehensive ground test data, component serial numbers, manufacturing database to construct a unified, definitive source of truth for aircraft health and component tracking. To solve these complex challenges, we foster an innovative environment where you are actively encouraged to leverage the latest technologies and state\-of\-the\-art AI frameworks to accelerate your work.
Responsibilities
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- Develop Health Algorithms: Design, build, and validate data\-driven and physics\-informed models to evaluate the condition, degradation, and Remaining Useful Life (RUL) of critical Joby subsystems (e.g., propulsion, batteries, actuation, and structures)
- Partner with Subject Matter Experts (SMEs): Collaborate closely with domain experts across Flight Physics, Aircraft Design, Flight Test, Reliability, and Systems Engineering to translate physical failure modes and structural loads into actionable diagnostics and prognostic algorithms
- Characterize Physical Behavior \& Operational Loads: Deeply analyze aircraft physical behavior and actual operational loads by wrangling complex sensor and time\-series data from flights, simulators, and subsystem test rigs. Use these insights to isolate anomalies, detect early faults, and map the long\-term degradation of critical components
- Component Usage Tracking \& Damage Modeling: Develop algorithmic frameworks to track component\-level operating metrics, flight cycles, and life limits. Translate real\-world operational loads into cumulative fatigue/damage models to monitor and inform fleet\-wide asset component replacement
- Write Production\-Grade Code: Turn prototypes into clean, well\-tested, maintainable, and production\-ready Python code. Participate in and actively raise the bar for team code reviews and engineering best practices
- Own Pipeline Architecture: Design, build, and own robust, end\-to\-end data pipelines and services that scale efficiently to process massive volumes of raw flight and test data
- Support Flight \& Field Validation: Work alongside test engineers and technicians to validate and harden health\-monitoring solutions using real\-world physical tests
- Drive Tooling Innovation: Selectively evaluate and integrate advanced ML/AI methodologies (such as automated data labeling or diagnostic assistance tooling) where they genuinely accelerate Prognostics Health Monitoring (PHM) workflows and team efficiency
Required
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- MS or PhD in Aerospace, Mechanical, Electrical Engineering, Computer Science, or a related technical field
- 3\+ years of post\-graduate experience (or equivalent) focused on PHM, Condition\-Based Maintenance (CBM\+), or the analysis of complex electro\-mechanical systems
- Exceptional, production\-quality Python skills (pandas, scipy, numpy, pyspark) with a strict focus on automated testing, CI/CD pipelines, and disciplined version control (Git)—not just Jupyter notebook prototyping
- Self\-driven, intellectually curious, and eager to learn and adopt new technologies
- Demonstrated ability to independently own implementation architecture and project lifecycles from ingestion to deployment with minimal supervision
- Demonstrable foundations in signal processing, time\-series analysis, and frequency\-domain fundamentals necessary to interpret physical sensor data
- Strong background in data analysis (algorithms, data structures, and architectures), probability, statistics, signal processing and predictive modeling
- Proven experience applying regression, neural networks, and machine/deep learning specifically for anomaly detection and fault isolation in physical hardware
- Experience leveraging Apache Spark or similar big data tools to wrangle, process, and analyze massive flight and test datasets. Experience with Databricks is a strong plus
- Strong collaborative and communication skills, with a track record of effectively working alongside multidisciplinary engineering teams
Desired
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- Deep understanding of rotating machinery diagnostics, vibration analysis, and aerospace failure modes. Familiarity with HUMS/AHM/IVHM certification processes is a massive plus
- Hands\-on experience applying Large Language Models (LLMs), agentic frameworks, Retrieval\-Augmented Generation (RAG), or advanced prompt engineering to accelerate technical workflows, automate data labeling, or build internal engineering assistance tools
- Experience building, monitoring, and maintaining ML pipelines in a high\-stakes, safety\-critical professional production environment
- Strong familiarity with relational databases (SQL, PostgreSQL) and designing custom APIs to seamlessly fetch and manipulate distributed data
Additional Information
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Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $147,200 \- $179,800/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. Joby is an Equal Opportunity Employer
Salary Context
This $147K-$179K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% 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 $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($163K) sits 15% below the category median. Disclosed range: $147K to $179K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Joby Aviation AI Hiring
Joby Aviation has 1 open AI role right now. They're hiring across Data Scientist. Based in Santa Cruz, CA, US. Compensation range: $179K - $179K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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