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About This Role
About the role
Muon Space is building a family of infrared imaging missions designed for global wildfire detection and monitoring, weather and cloud characterization, and space-based environmental monitoring. Muon's IR Data Products team builds the analytics system that processes the data from our spacecraft's custom multispectral imaging instruments into data products and analytics for diverse end-user stakeholders from these missions. We are seeking a Geospatial Data Scientist to join this team with a focus on building derived data products from Muon's FireSat and Space-Based Environmental Monitoring (SBEM) missions.
This role will be a hands-on contributor to the development of derived data products from Muon's IR data missions. You will dive deep into real-world data from brand new imaging systems, prototype and productionize algorithms that turn Level-1 imagery into higher-level derived products, and write production-ready code that feeds directly into customer-facing services. Working alongside senior scientists and product owners on the IR Data Products team, you will build expertise across both missions while contributing to product design, validation, and documentation. This is a high-impact, multi-mission role that sits at the intersection of rigorous Earth observation science and fast-moving product development, and is well-suited for someone who thrives on hands-on technical work and cross-functional collaboration.
This position is hybrid and requires working on-site in our Denver, CO office three days per week.
Responsibilities
- Build derived data products from Muon's infrared imaging missions, spanning Muon's IR programs
- Implement and improve scalable algorithms for processing, transforming, and analyzing multispectral infrared imagery into derived geospatial products.
- Contribute to derived product design and roadmap under the direction of senior scientists and product owners on the IR Data team.
- Contribute to technical and customer-facing design documents for derived products, supporting alignment between internal capabilities and external stakeholder requirements.
- Support engagement with customer technical stakeholders including Technical Interchange Meetings (TIMs), customer questions, and program meetings
- Write production-ready code that integrates into customer-facing data product pipelines and operational services.
- Contribute to the scientific quality, quantitative validation, and integrity of derived products across Muon's IR missions.
- Work cross-functionally with software engineers, calibration scientists, and instrument engineers to design, build, and maintain operational data product services.
- Learn new topics as needed (e.g., sensor geometry and characterization, georeferencing techniques, wildfire and environmental monitoring applications, cloud-native geospatial data engineering).
Qualifications
- Degree & Experience: PhD; or MS with 3+ years industry experience; or BS with 5+ years industry experience
- Geospatial Processing: Hands-on experience building geospatial data products, particularly from new or novel imaging instruments.
- Scientific Rigor: Demonstrated experience applying first principles to solve complex problems.
- Sensor Data: Experience working with real-world data from new or novel imaging instruments
- Software Development: Strong skills in Python-based development and analysis
- Geospatial Technical Stack: Strong Python development skills utilizing standard geospatial formats and libraries (e.g., COGs, Xarray, GDAL).
- Communication & Collaboration: Excellent written and verbal communication skills, with a track record of collaborating across distributed, interdisciplinary teams (scientists, engineers, product).
- Distributed Team: Ability to work with a distributed, interdisciplinary team (scientists, engineers, data support)
- Startup Adaptability: Ability to prioritize, pivot, and thrive in a fast-paced environment.
Preferred Qualifications
- Infrared sensing: Experience processing multispectral or infrared satellite imagery
- Domain Expertise: Domain expertise in wildfire monitoring, meteorology, weather, environmental remote sensing, or related Earth observation applications
- Mission & Operational Lifecycle: Experience supporting satellite mission product development, commissioning, early operations, or delivering operational pipelines to external customers.
- ML/AI: Demonstrated experience applying ML/DL and advanced image processing to remote sensing data
- Cloud Architecture: Familiarity with cloud-native geospatial workflows and large-scale data processing (AWS preferred).
- Technical Documentation: Experience authoring technical design documents, product specifications, or algorithm theoretical basis documents (ATBDs).
Salary
The salary range for this role is $171,000 - $191,000, plus a competitive equity grant and comprehensive benefits package. Final compensation will be determined based on skills, qualifications, experience, and geographic location as assessed during the interview process.
About Muon Space
Founded in 2021, Muon Space is an end-to-end Space Systems Provider that designs, builds, and operates LEO satellite constellations delivering mission-critical data. Our revolutionary, integrated technology stack enables customers to optimize every dimension of their missions for faster time-to-orbit and superior constellation remote sensing performance. Our state-of-the-art facility in the heart of Silicon Valley is optimized for manufacturing spacecraft and rapid, flexible payload integration at scale. From climate monitoring to national security, Muon Space is dedicated to delivering Earth Intelligence for a safer and more resilient world.
Taking Care of Our Team
At Muon salary is only part of our total compensation package. In addition to salary, full-time employees receive equity compensation as well as benefits including medical, dental, and vision insurance, a 401k retirement plan, short & long term disability and life insurance. We also offer three weeks paid vacation for new employees, along with 12 paid holidays, unlimited sick time and paid parental leave.
Our mission embraces the entire planet and we believe our team should too. Muon is dedicated to creating a diverse and dynamic company and workforce. We believe in equal employment opportunity regardless of race, color, ancestry, religion, sex, age, national origin, citizenship, sexual orientation, marital status, disability, or gender identity. We value diversity in the workplace, and that starts with our applicants. We encourage you to apply, even if you don't check all the boxes, and we look forward to reviewing your application! In addition, if you need a reasonable accommodation as part of your application for employment or interviews with us, please let us know.
Muon is currently registered and able to hire in the following states:
AL, AZ, CA, CO, CT, DC, FL, GA, IL, IA, ID, MD, MA, MI, MN, MO, NJ, NY, NC, OH, OR, PA, TN, TX, VT, VA, WA
ITAR/EAR Requirements
This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful, permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. The Company may, for legitimate business reasons, decline to pursue any export licensing process.
Salary Context
This $171K-$191K range is above the median for Data Scientist roles in our dataset (median: $167K across 172 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 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Muon Space, 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 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($181K) sits 5% below the category median. Disclosed range: $171K to $191K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Muon Space AI Hiring
Muon Space has 1 open AI role right now. They're hiring across Data Scientist. Based in Denver, CO, US. Compensation range: $191K - $191K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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