Senior Staff Data Scientist

About the role

Data-driven insights steer Mozilla's most important business choices in this Senior Staff role. The position transforms unclear problems into clear, data-driven recommendations that guide long-term strategy.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Rigorous analysis shapes key business choices and turns ambiguous situations into recommendations for executive strategy. This work directly influences Mozilla's long-term direction through data-driven guidance.

Trusted partnerships with senior leaders and cross-functional collaborators surface tradeoffs and align differing viewpoints. The team accelerates better, faster decisions by building relationships that enable clear tradeoff understanding.

Technical leadership within the Data Science community sees a senior role advising managers on technical direction.

Requirements

Clear communication converts complex findings into decision-ready recommendations for both technical and executive audiences while preserving analytical rigor. This skill ensures stakeholders at all levels understand insights and implications.

Influence without authority drives alignment across cross-functional teams through expertise-based trust. Leading through influence rather than formal authority guides teams toward shared decisions.

Technical mastery demonstrates deep expertise in Python or R, SQL, statistics, and modern data science tooling. Sound judgment about method selection guides application of emerging technologies.

A minimum of 25 years of relevant professional experience is required for this role.

A Bachelor's degree or equivalent practical experience forms the baseline qualification for the position.

Practical notes

Compensation for remote roles aligns with US Tier 1, Tier 2, and Tier 3 locations within the stated ranges. Performance-based bonus plans apply to eligible employees alongside rich medical, dental, and vision coverage.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data scientists use Python and SQL to build analytical solutions and modern data science tooling.

Work often involves ambiguous, large-scale problems where sound judgment about methods determines success.

Collaboration spans cross-functional partners and senior executives who rely on data-driven recommendations.

Remote work is available within designated US locations with compensation aligned to regional tiers.

This role mentors other data scientists and influences strategy across the organization.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.