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About This Role
Why Mozilla?
Mozilla Corporation is the non\-profit\-backed technology company that has shaped the internet for the better over the last 25 years. We make pioneering brands like Firefox, the privacy\-minded web browser. Now, with more than 225 million people around the world using our products each month, we're shaping the next 25 years of technology and helping to reclaim an internet built for people, not companies. Our work focuses on diverse areas including AI, social media, security and more. And we're doing this while never losing our focus on our core mission to make the internet better for people.
The Mozilla Corporation is wholly owned by the non\-profit 501(c) Mozilla Foundation. This means we aren't beholden to any shareholders—only to our mission. Along with thousands of volunteer contributors and collaborators all over the world, Mozillians design, build and distribute open\-source software that enables people to enjoy the internet on their terms.
About this team and role:
The Data Science organization shapes Mozilla's most important business decisions through data. As a Senior Staff (P5\) Data Scientist, you will be an analytical leader, partnering with senior executives to turn ambiguity into clear, data\-driven recommendations that influence Mozilla's long\-term strategy.
What you'll do:
- Strategic Influence: Shape Mozilla's most important business decisions with rigorous, data\-driven insights. Turn ambiguity into clear recommendations that influence executive thinking and long\-term strategy.
- Trusted Partnerships: Build trusted relationships with senior leaders and cross\-functional partners. Surface tradeoffs, align stakeholders with differing perspectives, and help teams make better, faster decisions.
- Technical Excellence: Design and deliver sophisticated analytical solutions to solve complex, ambiguous problems involving large\-scale, high\-dimensional data. Set a high technical bar through sound methodology, statistical rigor, and pragmatic execution.
- Mentorship and Growth: Serve as a technical leader within the Data Science community. Mentor data scientists, advise managers on technical direction, and elevate the quality of analytical work across the organization.
What you'll bring:
- Exceptional Communication: Outstanding verbal and written communication skills, with the ability to translate complex technical findings into clear, decision\-ready recommendations while preserving analytical rigor. Proven success communicating effectively with both technical and executive audiences.
- Influence Without Authority: A track record of driving alignment across cross\-functional teams, earning trust through expertise, and leading through influence rather than formal authority. Experience across multiple industries, domains, or problem spaces is a plus.
- Technical Mastery: Deep expertise in Python or R, SQL, statistics, and modern data science tooling. A strong foundation in analytical methods, paired with curiosity for emerging technologies and sound judgment about when to apply them.
What you’ll get:
- Generous performance\-based bonus plans to all eligible employees
- Rich medical, dental, and vision coverage
- Generous retirement contributions with 100% immediate vesting
- Quarterly all\-company wellness days where everyone takes a pause together
- Country specific holidays plus a day off for your birthday
- One\-time home office stipend
- Annual professional development budget
- Quarterly well\-being stipend
- Considerable paid parental leave
About Mozilla
Mozilla exists to build the Internet as a public resource accessible to all because we believe that open and free is better than closed and controlled. When you work at Mozilla, you give yourself a chance to make a difference in the lives of Web users everywhere. And you give us a chance to make a difference in your life every single day. Join us to work on the Web as the platform and help create more opportunity and innovation for everyone online.
Commitment to diversity, equity, inclusion, and belonging
Mozilla understands that valuing diverse creative practices and forms of knowledge are crucial to and enrich the company’s core mission. We encourage applications from everyone, including members of all equity\-seeking communities, such as (but certainly not limited to) women, racialized and Indigenous persons, persons with disabilities, persons of all sexual orientations, gender identities, and expressions.
We will ensure that qualified individuals with disabilities are provided reasonable accommodations to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment, as appropriate. Please contact us at hiringaccommodation@mozilla.com to request accommodation.
We are an equal opportunity employer. We do not discriminate on the basis of race (including hairstyle and texture), religion (including religious grooming and dress practices), gender, gender identity, gender expression, color, national origin, pregnancy, ancestry, domestic partner status, disability, sexual orientation, age, genetic predisposition, medical condition, marital status, citizenship status, military or veteran status, or any other basis covered by applicable laws. Mozilla will not tolerate discrimination or harassment based on any of these characteristics or any other unlawful behavior, conduct, or purpose.
Group: C
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Req ID: R3163
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 Mozilla Corporation, 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.
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.
Mozilla Corporation AI Hiring
Mozilla Corporation has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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