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About This Role
San Mateo, CA, United StatesData Science \& Analytics ID: 6320
Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.
At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.
A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.
### WHY DATA SCIENCE \& ANALYTICS?
The Data Science \& Analytics organization's mission is to increase our speed, frequency and acumen of making decisions at scale by instilling a data\-influenced approach to building products. We cover a wide area of the data spectrum including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling and machine learning. Aligned and partnering with product groups, we use this vast tool belt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products and measure the impact on our community of players and developers.
### WHY CONSUMER FRONTEND?
Consumer Frontend’s team mission is to deliver the frontend architecture, components, libraries, navigation patterns, and core UI necessary for a high\-quality, performant, intuitive, and observable consumer user experience for gaming and all future use cases. One of the focus areas is UX Excellence through Standardization, where the goal is to maintain a comprehensive library of high\-quality components and navigation patterns that ensure a consistent and intuitive experience across all consumer surfaces.
As a Data Scientist on the team, you will partner with engineers, product managers, designers, and leaders across the company to shape the future of user navigation, discovery, and interaction across the Roblox app. This includes helping define and evaluate foundational information architecture, navigation systems, and cross\-surface user experiences, while also contributing to the measurement strategy for emerging AI\-enabled workflows that accelerate how user experiences are designed, built, tested, and iterated.
This area is pivotal to Roblox’s long\-term success, directly impacting user engagement, retention, and the overall quality of the consumer experience. You will define KPIs and measurement frameworks for navigation efficiency, content discoverability, user friction, and UX quality, and leverage analytics, experimentation, and statistical rigor to influence product decisions and drive continuous improvements to the usability and effectiveness of the Roblox app.
You Will:
- Define and measure the success of Roblox’s information architecture, navigation systems, and core user experiences. Develop metrics and frameworks that quantify navigation efficiency, information discoverability, user friction, and overall UX quality.
- Develop a deep understanding of user journeys, navigation behaviors, and user intent across the Roblox app. Identify opportunities to improve how users discover content, navigate between surfaces, and accomplish their goals, and translate those insights into product strategy and UX improvements.
- Establish decision\-making frameworks for evaluating trade\-offs across user journeys, surfaces, and product objectives. Help teams understand the downstream effects of navigation and information architecture changes, balancing local optimizations with the health and effectiveness of the broader consumer experience.
- Leverage experimentation, causal inference, and statistical modeling to evaluate platform\-wide initiatives and cross\-surface UX investments. Design rigorous approaches to measurement in environments where traditional experimentation may be challenging.
- Partner closely with Product, Engineering, Design, and Data Science leaders across Consumer Apps and adjacent organizations to influence product strategy and shape the future of how users navigate and interact with Roblox.
- Perform deep\-dive analyses and build scalable measurement frameworks that enable teams to monitor UX health, identify regressions, uncover emerging opportunities, and make data\-informed decisions at scale.
You Have:
- An advanced degree in Statistics, Applied Mathematics, Physics, Engineering, Computer Science, Economics, or a related quantitative field.
- 5\+ years of industry experience in Data Science or a related quantitative discipline, preferably in consumer\-facing products or consumer applications.
- Strong analytical and technical skills, including experience working with large\-scale datasets and proficiency in SQL and Python/R.
- Deep expertise in statistical analysis, causal inference, experimentation, and measurement methodologies, with a track record of applying these techniques to influence product strategy and business outcomes.
- Demonstrated ability to independently drive ambiguous, high\-impact problem spaces, applying first\-principles thinking to develop novel frameworks and solutions.
- Experience partnering cross\-functionally with Product, Engineering, Design, and leadership teams to shape strategy, prioritize investments, and drive organizational alignment.
- Exceptional communication and storytelling skills, with the ability to distill complex, high\-dimensional problems into clear, actionable insights for both technical and executive audiences.
For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job\-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full\-time employees are also eligible for equity compensation and for benefits as described on this page.
Annual Salary Range
$263,670—$322,820 USD
Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).
Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.
For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H\-1B sponsorship at this time.
Salary Context
This $263K-$322K range is above the 75th percentile for Data Scientist roles in our dataset (median: $160K across 245 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 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Roblox, 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 $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($293K) sits 48% above the category median. Disclosed range: $263K to $322K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Roblox AI Hiring
Roblox has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in San Mateo, CA, US. Compensation range: $16K - $457K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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