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About This Role
CHAOS Industries is redefining modern defense with a multi\-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats.
CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. For more information, please visit www.chaosinc.com.
About the Team: Mission Engineering at CHAOS turns simulation output into decisions. We run large\-scale modeling and simulation campaigns across all warfighting domains and the full kill chain, against named threats, in operationally relevant scenarios, at the speed engineering, operational, and customer teams actually need. Every CHAOS engineering trade, pursuit, and customer engagement is anchored in rigorous, physics\-based, tactically relevant, and statistically valid analysis, and we're scaling the function to meet that bar across a growing product portfolio.
About the Role: You will own statistical methodology for the Mission Engineering team at CHAOS. You'll design experimental constructs that extract meaningful signals from broad trade studies and computationally expensive simulation runs, build the analytical pipelines the team relies on, push the methodological state of the art on how we characterize uncertainty, build surrogate models, and communicate quantitative results to decision\-makers. You will work shoulder\-to\-shoulder with engineers and experts in every domain to ensure that simulated, experimental, and tactical results presented by CHAOS are rigorous, reproducible, and actually deliver answers that our teams, customers, and partners need
This is a foundational hire. You will have the freedom to move fast and set the standards for how CHAOS does quantitative analysis from day one.
What You'll Do:
- Design rigorous experimental constructs (DOE, space\-filling designs, adaptive sampling, sequential experimentation) for large\-scale simulation campaigns, getting maximum signal per simulation hour across operationally relevant trade spaces.
- Apply advanced statistical methods (such as regression modeling, Bayesian inference, surrogate/metamodeling, sensitivity analysis, uncertainty quantification, and beyond) to simulation output to produce decision\-quality conclusions.
- Build and own scalable Python\-based data pipelines for ingestion, processing, statistical analysis, and visualization of large simulation datasets.
- Develop ML and statistical surrogate models that accelerate analysis, enable real\-time trade studies, and feed mission planning applications.
- Set team standards for data management, reproducibility, and statistical rigor (such as code review, methodology validation, and documentation practices).
- Translate operational and engineering questions into well\-structured analytical approaches alongside M\&S engineers, threat SMEs, and program staff. Push back when the framing is wrong.
- Author technical reports and briefing materials with clear, honest data visualizations; present quantitative results to senior technical and non\-technical audiences in language they can act on.
- Mentor peers and cross\-functional teams on experimental design, statistical methodology, and reproducible analysis.
- Support programs spanning DoD services, DARPA, intelligence community, and commercial customers.
Required Qualifications:
- Bachelor's degree or higher in Statistics, Data Science, Mathematics, Artificial Intelligence, a related quantitative field, or equivalent demonstrated expertise in modern statistical methodology.
- 7\+ years applying advanced statistical and data science methods, ideally supporting defense, intelligence, or advanced technology programs.
- Deep working expertise in experimental design, regression and Bayesian methods, uncertainty quantification, and surrogate modeling, not just textbook familiarity.
- Strong proficiency working in Python, including scientific computing and ML libraries (especially Pandas, Polars, NumPy, SciPy, Scikit\-Learn, Statsmodels, PyMC, Matplotlib, Seaborn, CuPy, PyTorch), and exposure to MATLAB or R.
- Demonstrated experience building scalable analytical pipelines for large datasets, including comfort with terabyte\-scale data and modern dataframe tooling.
- Exceptional data visualization skills and the ability to develop briefing\-quality technical products.
- Strong software development practices: version control, code review, reproducible workflows, and informed use of AI\-assisted coding tools.
- Track record of working independently, taking ownership of ambiguous problems, and delivering with minimal oversight.
- Comfortable working with Linux operating systems and writing scalable scripts/software.
- Exceptional written and verbal communication skills, especially in translating quantitative approaches and results for non\-technical audiences.
- Eligibility to obtain a Top Secret / Sensitive Compartmented Information (TS/SCI) clearance.
Preferred Qualifications:
- Master's or PhD in Statistics, Data Science, Mathematics, Artificial Intelligence, or a related quantitative field.
- Direct experience applying statistical methods to outputs from military simulations including high fidelity engineering models, war games, and engagement or mission\-level combat simulations such as AFSIM, ESAMS, Brawler, or Ansys STK.
- Expertise designing and analyzing large\-scale Monte Carlo and DOE\-driven simulation campaigns supporting full kill chain or system effectiveness assessment.
- Experience in developing surrogate models, simulations, machine learning, or artificial intelligence models for engineering and operations analysis applications.
- Familiarity with sensor performance analysis (radar, EO/IR, RF, acoustic), weapon effectiveness analysis, or mission\-level engagement analysis.
- Experience with HPC environments and distributed computing frameworks (including scalable cloud services and GPU\-accelerated computing).
- Leadership experience: mentoring or leading project teams through complex analytical efforts.
- Substantial experience in communicating statistical methods to both technical and non\-technical stakeholders and decisionmakers.
- Experience supporting rapid development programs for DoD contractors, combatant commands, research labs, and acquisition communities.
- Active TS/SCI clearance.
Why CHAOS?
- Health Benefits: Medical, dental, and vision benefits 100% paid for by the company
- Additional benefits: 401k (\+ 50% company match up to 6% of pay), FSA, HSA, life insurance, and more
- Our Perks: Free daily lunch, 'No meeting Fridays', unlimited PTO, casual dress code
- Compensation Components: Competitive base salaries, generous pre\-IPO stock option grants, relocation assistance, and (coming soon!) annual bonuses
- Team Growth: 250 employees and counting across 5 global offices
*Salary Range: $140K\-220K*
*The stated compensation range reflects only the targeted base compensation range and excludes additional earnings such as bonus, equity, and benefits. If your compensation requirements fall outside of the range, we still encourage you to apply. The salary range for this role is an estimate based on a range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations.*
### Recruiting Agencies: CHAOS Industries does not accept unsolicited resumes or outreach. Unsolicited submissions will not be reviewed or compensated.
*\#LI\-onsite*
Salary Context
This $140K-$220K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At CHAOS Industries, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($180K) sits 7% below the category median. Disclosed range: $140K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CHAOS Industries AI Hiring
CHAOS Industries has 1 open AI role right now. They're hiring across Data Scientist. Based in Los Angeles, CA, US. Compensation range: $220K - $220K.
Location Context
AI roles in Los Angeles pay a median of $214,112 across 708 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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