What is a Data Scientist?

At Discord, a Data Scientist is not just a number cruncher; you are a strategic partner in building a place where hundreds of millions of people connect, play, and belong. Whether you are focused on Product Analytics, Strategic Research, or the growing Ads ecosystem, your work directly influences how we understand user behavior and how we shape the future of the platform. You will be dealing with massive scale—over 1.5 billion hours of gameplay are tracked monthly—requiring you to distill complex, petabyte-scale data into clear, actionable narratives.

You will work on nimble, cross-functional squads alongside Product Managers, Engineers, and Designers. Your role is to bring scientific rigor to decision-making, ensuring that we aren't just shipping features, but solving real user problems. From optimizing the "Go Live" streaming experience to building the analytical backbone of our privacy-first Ads business, you will define the metrics that matter. You are the voice of truth in the room, using data to advocate for the user experience while driving sustainable business growth through Nitro and advertising partnerships.

Getting Ready for Your Interviews

Preparing for a Data Science interview at Discord requires a shift in mindset. We are less interested in your ability to memorize textbook definitions and more interested in how you apply technical skills to ambiguous, open-ended problems. You need to demonstrate that you can think like a product owner and execute like a scientist.

You will be evaluated on the following key criteria:

Product Intuition & Metric Definition – This is critical. You must demonstrate the ability to translate vague business goals (e.g., "increase community engagement") into concrete, measurable metrics. Interviewers will test if you understand the nuances of the Discord ecosystem, such as the difference between a user simply logging in versus participating in a voice channel or streaming a game.

Technical Execution (SQL & Analytics) – We expect fluency in manipulating data. You will face scenarios requiring complex SQL queries to extract insights from large datasets. You need to show that you can write efficient code that handles scale, particularly using tools like BigQuery, and that you can visualize your findings effectively using tools like Looker.

Experimentation (A/B Testing) – You must understand how to design valid experiments in a networked environment. We look for candidates who understand statistical significance, power analysis, and the specific challenges of testing on a social platform where one user's behavior influences another (network effects).

Communication & Data Storytelling – Your analysis is only as good as your ability to explain it. You will be evaluated on how well you can communicate complex technical findings to non-technical stakeholders, such as Sales leaders or Product Managers, and how you use data to influence strategy.

Interview Process Overview

The interview process for Data Scientists at Discord is designed to be rigorous yet conversational. It generally begins with a recruiter screen to align on your background and interest in the role. This is followed by a technical screen, which typically focuses on SQL proficiency and basic probability or product sense. This stage is a filter to ensure you have the raw technical skills required to handle our data infrastructure.

If you pass the screen, you will move to the Virtual Onsite. This is a comprehensive loop consisting of 3 to 4 separate rounds. You can expect a mix of interviews covering Product Analytics (case studies), Advanced SQL/Data Manipulation, Statistics/Experimentation, and a Behavioral round focused on our values. The "Product Sense" interviews are often the most challenging, as they require you to simulate the day-to-day decision-making of a Discord Data Scientist. We value candidates who are "users first" and can empathize with the gaming and community-building aspects of our platform.

This timeline illustrates the typical flow from application to offer. Note that the "Technical Screen" is often a live coding environment (usually CoderPad or similar) where you will write SQL against a provided schema. The Onsite rounds are designed to test depth; you will not just be asked how to calculate a metric, but why that metric is the right choice and what the potential pitfalls are.

Deep Dive into Evaluation Areas

Your interviews will break down into specific areas of competency. Success here requires moving beyond surface-level answers to demonstrate deep understanding.

Product Analytics & Metric Sense

This is arguably the most important non-coding section. You will be given an open-ended scenario related to a Discord feature (e.g., Voice Channels, Go Live, Server Discovery) or a business initiative (e.g., Ads Revenue, Nitro Churn).

Be ready to go over:

• Success Metrics: How to define a "North Star" metric for a feature and identify secondary metrics to track health.
• Counter-metrics: Identifying what could go wrong if you optimize for a single metric (e.g., increasing ad load might decrease user retention).
• Funnel Analysis: diagnosing where users drop off in a specific flow, such as the new user onboarding process or the Nitro checkout flow.
• Ecosystem Impact: Understanding how a change in one part of the app (e.g., text chat) impacts another (e.g., voice chat).

Example questions or scenarios:

• "We want to launch a new feature that allows users to gift Nitro to friends. How would you measure its success?"
• "Voice connection quality has improved, but total talk time is down. How would you investigate this?"
• "Define 'active user' for a specific Discord community. How does this differ from a general DAU?"

SQL & Data Manipulation

You will be expected to write SQL queries to solve business questions. The data schema will likely mimic Discord’s structure (Users, Servers, Channels, Messages).

Be ready to go over:

• Joins and Aggregations: Proficiency with LEFT JOIN, INNER JOIN, and grouping data by multiple dimensions (time, region, device).
• Window Functions: Using RANK(), LEAD(), LAG(), and moving averages to analyze user behavior over time.
• Data Cleaning: Handling NULL values, duplicates, and messy timestamp formats.
• Query Optimization: Writing queries that are not just correct, but performant enough for Big Data environments (BigQuery).

Example questions or scenarios:

• "Given a table of user logins and a table of server joins, calculate the retention rate of users who joined a server on their first day."
• "Write a query to find the top 3 games played by users in each country for the last month."
• "Calculate the week-over-week growth rate of messages sent per server."

Statistics & Experimentation

Discord relies heavily on A/B testing to make product decisions. You need to show you can run a rigorous experiment from design to analysis.

Be ready to go over:

• Hypothesis Testing: Formulating a clear null and alternative hypothesis.
• Sample Size & Power: calculating how long an experiment needs to run to detect a specific lift.
• Network Effects (Interference): Discussing how to randomize users when the treatment effect might spill over to the control group (e.g., if I get a new video feature, it affects my friends who are in the control group).
• Bias & Validity: Identifying selection bias or novelty effects that might skew results.

Example questions or scenarios:

• "We ran an A/B test on the 'Add Friend' button color, and clicks went up 5%, but p-value is 0.07. What is your recommendation?"
• "How would you design an experiment for a feature that affects an entire server, not just an individual user?"
• "How do you handle a situation where two experiments are running on the same page at the same time?"

The word cloud above highlights the frequency of topics reported by candidates. Notice the dominance of SQL, Metrics, Experimentation, and Product. This confirms that while statistical knowledge is required, the application of that knowledge to product questions (Product Sense) and the ability to retrieve the data (SQL) are the primary pillars of the interview.

Key Responsibilities

As a Data Scientist at Discord, your day-to-day work balances proactive exploration with reactive problem solving. You are responsible for building the analytical foundation for your specific product area—whether that is Ads, Growth, or Safety. This involves writing robust ETL pipelines (often in collaboration with Data Engineering) to ensure data quality and building self-serve dashboards in Looker so stakeholders can monitor business health independently.

Collaboration is central to the role. You will partner with Product Managers to develop learning agendas—roadmaps of what we need to learn about our users to make strategic decisions. For the Ads team specifically, you will focus on building measurement solutions that respect user privacy while proving value to advertisers. This might involve designing incrementality tests or analyzing auction dynamics. For Senior and Staff roles, you are also expected to lead data governance initiatives, ensuring that the metrics we report to executives are consistent, accurate, and meaningful across the entire company.

Role Requirements & Qualifications

To be competitive for this role, you need a blend of technical expertise and business acumen.

Must-have skills:

• Advanced SQL: You must be comfortable writing complex queries from scratch. Experience with BigQuery is a significant plus.
• Product Analytics Experience: Proven history of translating business questions into data analysis and actionable recommendations.
• Experimentation: 3+ years (5+ for Senior roles) of experience designing and analyzing A/B tests.
• Visualization: Proficiency in building production-grade dashboards using Looker, Tableau, or similar tools.
• Communication: Ability to explain technical concepts (like statistical significance or attribution windows) to non-technical partners.

Nice-to-have skills:

• Ad Tech Experience: For Ads roles, familiarity with CPM, CTR, ROAS, and attribution models is highly valued.
• Gaming/Community Knowledge: Passion for Discord or the gaming industry helps you build better intuition for user behavior.
• Privacy-First Measurement: Experience with differential privacy or aggregated measurement frameworks.
• Python/R: While SQL is primary, proficiency in Python (pandas, numpy) for more advanced statistical analysis is often expected for higher-level roles.

Common Interview Questions

The following questions are representative of what you might face. They are drawn from candidate data and are designed to test your ability to apply your skills to Discord's specific challenges.

Product Sense & Metrics

• "We noticed a drop in voice channel usage last Tuesday. How would you investigate the cause?"
• "How would you measure the success of a new 'Spoiler Tag' feature for images?"
• "If we introduce ads to the platform, what health metrics would you monitor to ensure we aren't hurting user retention?"
• "What metrics would you look at to determine if a server is 'healthy' or 'dead'?"

SQL & Technical

• "Write a query to identify users who are 'power users' based on their message frequency and voice participation."
• "Given a table of ad impressions and clicks, calculate the Click-Through Rate (CTR) per campaign for the last 7 days."
• "Find the retention rate of users who purchased Nitro within 24 hours of account creation."
• "How would you debug a dashboard that is showing conflicting numbers compared to the source table?"

Statistics & Probability

• "Explain the difference between a one-sided and two-sided hypothesis test. When would you use each?"
• "How do you determine the minimum sample size needed for an experiment?"
• "We want to test a new video compression algorithm. What metrics do we track, and how do we ensure the test is valid?"

Behavioral & Culture

• "Tell me about a time you had to convince a stakeholder that their hypothesis was wrong using data."
• "Describe a situation where you had to prioritize between speed of analysis and accuracy."
• "Why Discord? How do you use the platform?"

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Frequently Asked Questions

Q: How technical is the interview process? The process is quite technical regarding data extraction and interpretation. You must be fluent in SQL. However, it is not typically an algorithmic coding interview (like LeetCode for software engineers). You likely won't be asked to invert a binary tree, but you will be asked to perform complex data joins and statistical calculations.

Q: Do I need to be a gamer to work at Discord? No, you don't need to be a "hardcore" gamer. However, you do need to understand the product and the community dynamics. Being a user of Discord helps significantly with "Product Sense" questions because you will intuitively understand concepts like "Servers," "Channels," and "Roles."

Q: What tools does the Data Science team use? We primarily use BigQuery for data warehousing and SQL, Looker for visualization and reporting, and Python (Jupyter Notebooks) for ad-hoc analysis and advanced modeling.

Q: Is this role remote? Many Data Science roles at Discord are listed as "Remote" or hybrid in the San Francisco Bay Area. Check the specific job posting, but Discord generally supports a flexible work environment.

Q: How should I prepare for the "Ads" specific roles? If you are interviewing for an Ads role, brush up on advertising fundamentals: auctions, inventory, fill rates, and attribution. You will be expected to apply general data science principles to these specific domain problems.

Other General Tips

Think in "Ecosystems": Discord is unique because it is a network of communities. When answering product questions, don't just think about the individual user; think about how a change affects the server and the relationships between users.

Data Governance Matters: Especially for Senior and Staff roles, show that you care about data quality. Mentioning how you would validate data sources, set up alerts for anomalies, or document metric definitions will set you apart as a mature practitioner.

Clarify Before You Solve: In the onsite case studies, never jump straight to a solution. Always ask clarifying questions to narrow the scope. For example, "Are we launching this globally or in one region?" or "Is this feature for mobile or desktop users?"

Summary & Next Steps

The Data Scientist role at Discord offers a rare opportunity to work on a product that genuinely shapes internet culture. You will be solving complex problems at a massive scale—from optimizing real-time communications to building a next-generation ads platform that respects user privacy. The work you do here will directly impact how millions of people find belonging online.

To succeed, focus your preparation on SQL fluency, product metrics, and experimental design. Be ready to think critically about why we measure what we measure. Approach the interviews with curiosity and a collaborative spirit. We are looking for people who can not only find the answer but also bring the team along on the journey.

The salary data above provides a baseline for the role. Note that compensation at Discord is competitive and includes significant equity components. For Senior and Staff roles, the range reflects the high expectation for technical leadership and strategic impact.

You have the roadmap—now it’s time to prepare. Good luck!