thatgamecompany Data Scientist Interview Guide

Getting ready for a Data Scientist interview at thatgamecompany? The thatgamecompany Data Scientist interview process typically spans a broad range of question topics and evaluates skills in areas like data analysis, statistical modeling, data engineering, and stakeholder communication. Interview preparation is especially important for this role, as candidates are expected to demonstrate expertise in analyzing complex user and gameplay data, developing actionable insights for growth, and clearly presenting findings to technical and non-technical audiences. Success in this interview requires not only technical proficiency but also an understanding of how data science drives product decisions and user experience in a creative, player-focused gaming environment.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Scientist positions at thatgamecompany.
• Gain insights into thatgamecompany’s Data Scientist interview structure and process.
• Practice real thatgamecompany Data Scientist interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the thatgamecompany Data Scientist interview process, along with sample questions and preparation tips tailored to help you succeed.

thatgamecompany is an independent video game developer renowned for creating emotionally impactful and artistically innovative games, including the critically acclaimed Sky: Children of the Light. Operating in the interactive entertainment industry, the company is dedicated to fostering human connection and positive experiences through gameplay. With a global player base, thatgamecompany emphasizes community, creativity, and meaningful engagement. As a Data Scientist, you will play a crucial role in analyzing player behavior, optimizing user acquisition, and supporting the long-term growth and sustainability of their flagship titles, directly contributing to the company’s mission of inspiring empathy and connection.

As a Data Scientist at thatgamecompany, you will play a key role in supporting the growth and long-term success of the flagship title, Sky: Children of the Light. You will analyze complex player and business data to generate actionable insights, develop dashboards and reports, and collaborate with marketing, business intelligence, and game development teams to align data strategies with product goals. Core responsibilities include defining key metrics, optimizing user acquisition and retention through statistical modeling and experimentation, and maintaining data pipelines and attribution systems. Your work will directly inform decision-making and help expand the game’s global reach while upholding the studio’s creative vision.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough screening of your resume and application materials, focusing on your experience with data science in consumer apps or mobile games, proficiency in Python, SQL, and statistical modeling, and your ability to develop data pipelines, dashboards, and visualizations. The review team—typically comprised of HR and a data science team lead—looks for evidence of advanced analytics, experimentation (such as A/B testing), and effective communication of data-driven insights to diverse audiences. To prepare, ensure your resume highlights relevant projects involving user acquisition, retention analysis, and cross-functional collaboration.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for a 30–45 minute introductory call, aiming to assess your motivation for joining thatgamecompany, your alignment with the studio’s mission, and your general fit for the data scientist role. Expect questions about your career trajectory, experience with mobile game analytics, and your approach to making data accessible for non-technical stakeholders. Preparation should focus on articulating your impact in previous roles, your interest in gaming, and your ability to communicate complex findings simply.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one or two interviews led by senior data scientists or analytics managers. You’ll be asked to solve technical problems and case studies that mirror real challenges at thatgamecompany—such as designing experiments to measure marketing campaign success, building predictive models for player retention, or cleaning and integrating large-scale game data. You may need to demonstrate proficiency in Python or SQL, statistical analysis, and data visualization. Preparation should include reviewing ETL pipeline design, mobile attribution methodologies, and your experience with tools like Tableau or Looker.

2.4 Stage 4: Behavioral Interview

Led by cross-functional team members (game development, marketing, BI), the behavioral interview evaluates your collaboration skills, adaptability, and communication style. You’ll discuss how you’ve overcome obstacles in data projects, resolved stakeholder misalignments, and presented actionable insights to non-technical audiences. Be ready to share examples of translating technical findings into strategic recommendations and fostering data-driven decision-making across teams.

2.5 Stage 5: Final/Onsite Round

The final round generally consists of a series of in-depth interviews with leadership and potential teammates—sometimes including a live case presentation or technical deep dive. You may be asked to walk through end-to-end project examples, defend your modeling choices, and demonstrate your ability to design experiments and communicate results tailored to different audiences. This is also an opportunity to showcase your understanding of the gaming industry and your passion for player-centric analytics.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, the recruiter will present a formal offer and discuss compensation, benefits, and your potential start date. Salary negotiation and clarification of bonus structure, remote work options, and professional development support are handled at this stage.

2.7 Average Timeline

The typical interview process for a Data Scientist at thatgamecompany spans 3–5 weeks from application to offer. Fast-track candidates with strong gaming analytics backgrounds or direct experience in mobile games may progress in as little as 2–3 weeks, whereas standard pacing allows time for technical assessments, cross-team interviews, and scheduling flexibility. The final onsite round may require multiple sessions over several days, especially if presentations or live technical challenges are involved.

Next, let’s dive into the specific interview questions you may encounter throughout the process.

3.1 Analytics & Experimentation

Expect questions on designing experiments, measuring success, and drawing actionable insights from user and system data. Focus on how you would structure analyses to inform product decisions, including A/B testing and user segmentation.

3.1.1 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you would set up and interpret an A/B test, including control/treatment selection, metric definition, and statistical significance. Discuss how experiment results would influence business decisions.

Example answer: "I would first define a clear success metric, randomly assign users to control and treatment groups, and run the experiment long enough to reach statistical significance. After analyzing the results, I’d present findings with confidence intervals and actionable recommendations for the product team."

3.1.2 You work as a data scientist for a ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe how you would design the experiment, select relevant metrics (e.g., conversion, retention, profit), and monitor short- and long-term impacts.

Example answer: "I’d conduct a controlled experiment, tracking metrics like rider retention, order frequency, and overall profitability. I’d also analyze cohort behavior and present a recommendation based on both immediate and sustained effects."

3.1.3 We're interested in how user activity affects user purchasing behavior.
Discuss how you would analyze the relationship between activity and purchases, including cohort analysis or regression techniques.

Example answer: "I’d segment users by activity level and track their purchase rates over time, using logistic regression to quantify the effect of activity on conversion probability."

3.1.4 Let's say that you work at TikTok. The goal for the company next quarter is to increase the daily active users metric (DAU).
Outline strategies for analyzing DAU trends, identifying drivers, and proposing actionable interventions.

Example answer: "I’d analyze DAU by segment, identify key drivers of engagement, and recommend targeted product changes or campaigns to boost active usage."

3.2 Data Engineering & System Design

These questions assess your ability to architect data pipelines, design scalable systems, and organize complex datasets for analytics and modeling. Focus on efficiency, reliability, and clarity in your solutions.

3.2.1 Design a data warehouse for a new online retailer
Describe your approach to schema design, ETL processes, and supporting analytical queries.

Example answer: "I’d start by identifying key business entities, design normalized tables, and set up ETL jobs to ingest transactional and user data. The warehouse would support flexible reporting and machine learning downstream."

3.2.2 System design for a digital classroom service.
Explain how you’d design a scalable, reliable system for classroom data, considering real-time updates, analytics, and privacy.

Example answer: "I’d architect a modular system with separate layers for ingestion, processing, and analytics, ensuring data privacy and scalability for large numbers of students and teachers."

3.2.3 Ensuring data quality within a complex ETL setup
Discuss strategies for monitoring, validating, and remediating data quality issues in multi-source ETL pipelines.

Example answer: "I’d implement automated validation checks, anomaly detection, and reconciliation routines to maintain data integrity across sources."

3.2.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Describe your approach to data cleaning, integration, and cross-source analysis.

Example answer: "I’d profile each source for quality, align schemas, join on common keys, and use feature engineering to extract insights relevant to system improvement."

3.3 Machine Learning & Modeling

Expect questions on building, evaluating, and explaining predictive models tailored to business needs. Emphasize your understanding of model selection, feature engineering, and communicating results.

3.3.1 Building a model to predict if a driver on Uber will accept a ride request or not
Discuss how you would frame the problem, select features, and evaluate the model.

Example answer: "I’d use historical ride data to engineer features like location, time, and driver history, train a classification model, and evaluate it using ROC curves and precision-recall metrics."

3.3.2 Identify requirements for a machine learning model that predicts subway transit
Explain your approach to data collection, feature selection, and model evaluation for transit prediction.

Example answer: "I’d gather transit logs, weather, and event data, engineer time-series features, and validate model performance on unseen time windows."

3.3.3 Let's say that you're designing the TikTok FYP algorithm. How would you build the recommendation engine?
Describe your approach to recommendation systems, including data inputs, model types, and evaluation metrics.

Example answer: "I’d combine user interaction data with content features, train collaborative and content-based models, and optimize recommendations for engagement."

3.3.4 Build a random forest model from scratch.
Explain the steps to implement a random forest, including tree construction and aggregation.

Example answer: "I’d build multiple decision trees on bootstrapped samples, aggregate their predictions, and tune hyperparameters for optimal accuracy."

3.4 Data Cleaning & Feature Engineering

These questions probe your ability to preprocess raw data, handle missing values, and engineer robust features for analysis and modeling. Demonstrate practical techniques and clear communication of trade-offs.

3.4.1 Describing a real-world data cleaning and organization project
Share your process for cleaning messy data, resolving inconsistencies, and preparing datasets for analysis.

Example answer: "I’d profile the dataset, handle missing values, normalize formats, and document every step for reproducibility."

3.4.2 Challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in 'messy' datasets.
Describe how you would reformat and clean non-standard datasets to enable analysis.

Example answer: "I’d standardize layouts, address missing or ambiguous entries, and automate cleaning routines for future consistency."

3.4.3 Implement one-hot encoding algorithmically.
Explain the steps to transform categorical variables for modeling.

Example answer: "I’d identify categorical columns, generate binary indicator columns for each category, and ensure compatibility with downstream models."

3.4.4 Encoding categorical features
Discuss different encoding strategies and their trade-offs.

Example answer: "I’d choose between one-hot, label, or target encoding based on cardinality and model type, balancing interpretability and performance."

3.4.5 Adding a constant to a sample
Describe how adding a constant affects statistical properties of a dataset.

Example answer: "Adding a constant shifts the mean but does not affect variance or standard deviation, which is important for normalization and modeling."

3.5 Communication & Stakeholder Management

These questions focus on your ability to translate technical findings into actionable recommendations and manage expectations with non-technical stakeholders. Highlight clarity, adaptability, and influence.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss strategies for effective presentations, including storytelling and visualization.

Example answer: "I’d tailor my presentation using simple visuals, focus on actionable insights, and adjust my language to the audience’s technical level."

3.5.2 Demystifying data for non-technical users through visualization and clear communication
Describe how you make data accessible and actionable for all stakeholders.

Example answer: "I’d use intuitive charts, avoid jargon, and provide concrete recommendations linked to business goals."

3.5.3 Making data-driven insights actionable for those without technical expertise
Share your approach to simplifying complex analyses for decision-makers.

Example answer: "I’d break down findings into key takeaways, use analogies, and suggest clear next steps."

3.5.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Explain how you handle conflicting priorities and drive consensus.

Example answer: "I’d facilitate alignment meetings, clarify project goals, and communicate trade-offs transparently."

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
How to Answer: Describe the context, the data you analyzed, and the decision you influenced. Focus on the business impact and how your recommendation was implemented.
Example answer: "At my previous role, I analyzed user engagement patterns and recommended a UI change that led to a 15% increase in session length."

3.6.2 Describe a challenging data project and how you handled it.
How to Answer: Share a specific project, the obstacles you faced, and the steps you took to overcome them. Emphasize problem-solving and adaptability.
Example answer: "I managed a complex data integration project with inconsistent formats, developing automated scripts to standardize inputs and improve reporting accuracy."

3.6.3 How do you handle unclear requirements or ambiguity?
How to Answer: Explain your approach to clarifying objectives, iterating with stakeholders, and documenting assumptions.
Example answer: "I schedule early check-ins with stakeholders, ask targeted questions, and maintain a living requirements document to ensure alignment."

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
How to Answer: Highlight your communication and collaboration skills, showing how you sought consensus and incorporated feedback.
Example answer: "I presented my analysis, invited critique, and revised my approach based on team input, ultimately improving our solution."

3.6.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
How to Answer: Discuss how you prioritized requests, communicated trade-offs, and protected project timelines and data integrity.
Example answer: "I quantified the additional effort, presented trade-offs, and used a prioritization framework to keep the scope manageable."

3.6.6 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
How to Answer: Share how you identified repetitive issues and implemented automation to prevent future occurrences.
Example answer: "I built scheduled scripts to validate and clean incoming data, reducing manual errors and saving the team hours each week."

3.6.7 Tell us about a time you caught an error in your analysis after sharing results. What did you do next?
How to Answer: Demonstrate accountability, transparency, and your process for correcting and communicating mistakes.
Example answer: "After spotting a calculation error, I immediately notified stakeholders, corrected the report, and updated my workflow to prevent future mistakes."

3.6.8 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
How to Answer: Explain your process for rapid prototyping and how it facilitated consensus.
Example answer: "I created interactive wireframes to visualize analytics dashboards, helping stakeholders converge on a shared vision before development."

3.6.9 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
How to Answer: Discuss your prioritization framework and communication strategies.
Example answer: "I used business impact scoring and facilitated a prioritization workshop to ensure alignment across leadership."

3.6.10 Give an example of learning a new tool or methodology on the fly to meet a project deadline.
How to Answer: Highlight your resourcefulness and commitment to continuous learning.
Example answer: "Faced with a tight deadline, I taught myself a new data visualization tool over a weekend and delivered the project ahead of schedule."

4.1 Company-specific tips:

Immerse yourself in thatgamecompany’s mission and values, especially their focus on fostering human connection and emotional experiences through games. Be ready to articulate how data science can support these goals by enhancing player engagement and community-building.

Research the studio’s flagship title, Sky: Children of the Light, and explore its player community, in-game events, and monetization strategies. Develop a perspective on how data can be used to optimize user acquisition, retention, and player satisfaction in a free-to-play mobile game.

Familiarize yourself with the unique challenges of analyzing player behavior in games that are both creative and socially driven. Think about how you would measure success beyond traditional metrics, considering factors like player sentiment, social interactions, and long-term engagement.

Prepare examples of how you have previously aligned data initiatives with broader product or creative visions, especially in environments where empathy, inclusivity, and positive user experiences are prioritized.

4.2 Role-specific tips:

Demonstrate fluency in designing experiments and interpreting results, particularly in the context of A/B testing and user segmentation for mobile games. Be prepared to discuss how you would structure experiments to measure the impact of new features, events, or marketing campaigns on player retention and monetization.

Showcase your ability to work with complex data pipelines and ETL processes. Be ready to describe how you would integrate diverse data sources—such as gameplay telemetry, payment transactions, and marketing attribution—to generate unified insights for the team.

Highlight your experience with statistical modeling and machine learning, focusing on real-world scenarios like predicting player churn, segmenting user cohorts, or building recommendation systems for in-game content. Be specific about your approach to feature engineering and model evaluation in a gaming context.

Emphasize your communication skills by preparing stories that demonstrate how you have translated technical findings into actionable recommendations for non-technical stakeholders. Practice explaining complex analyses in simple, relatable terms and connecting your insights to business or creative outcomes.

Prepare to discuss your approach to data quality, especially within fast-moving, multi-source environments like mobile games. Share examples of how you have automated data validation, resolved inconsistencies, and ensured the reliability of analytics used for decision-making.

Anticipate behavioral questions about cross-functional collaboration, adaptability, and stakeholder management. Reflect on times you have navigated ambiguity, aligned differing priorities, or advocated for data-driven decisions in creative or product-driven teams.

Finally, express genuine enthusiasm for the intersection of data science and game design. Share your curiosity about player psychology, community dynamics, and how analytics can help create more joyful, meaningful gaming experiences for a global audience.

5.1 How hard is the thatgamecompany Data Scientist interview?
The thatgamecompany Data Scientist interview is challenging but rewarding, especially for candidates passionate about games and analytics. You’ll be expected to demonstrate advanced data analysis, statistical modeling, and experiment design skills, all within the context of player-centric gaming environments. The process also tests your ability to communicate insights to both technical and non-technical stakeholders, and your understanding of how data science can enhance user experience and drive creative product decisions.

5.2 How many interview rounds does thatgamecompany have for Data Scientist?
Typically, there are five to six rounds: initial resume screening, recruiter phone screen, technical/case interviews, behavioral interviews, final onsite or virtual interviews (which may include a live case presentation), and the offer/negotiation stage. Each round is designed to assess different facets of your technical expertise, business acumen, and cultural fit.

5.3 Does thatgamecompany ask for take-home assignments for Data Scientist?
Take-home assignments are occasionally part of the process, particularly for assessing your approach to real-world data problems relevant to gaming analytics. These may involve analyzing player behavior data, designing experiments, or developing dashboards to present actionable insights. The assignment is intended to showcase your technical skills and ability to communicate findings clearly.

5.4 What skills are required for the thatgamecompany Data Scientist?
Key skills include proficiency in Python and SQL, expertise in statistical modeling and experimentation (A/B testing, cohort analysis), experience with data pipeline and ETL design, and the ability to visualize and communicate insights. Familiarity with mobile game analytics, user acquisition/retention metrics, and cross-functional collaboration is highly valued. Experience with tools like Tableau or Looker, and a strong grasp of player psychology and community engagement, will set you apart.

5.5 How long does the thatgamecompany Data Scientist hiring process take?
The typical timeline is 3–5 weeks from application to offer. Fast-track candidates with direct gaming analytics experience may move faster, while standard pacing allows time for technical assessments and multiple team interviews. The process may extend if scheduling or additional case presentations are required.

5.6 What types of questions are asked in the thatgamecompany Data Scientist interview?
You’ll encounter a mix of technical, case-based, and behavioral questions. Expect challenges related to experiment design, player segmentation, retention modeling, data pipeline architecture, and system design. Behavioral interviews focus on stakeholder management, communication, and your ability to align data initiatives with creative product goals. You may also be asked about your experience handling ambiguity and driving consensus in cross-functional teams.

5.7 Does thatgamecompany give feedback after the Data Scientist interview?
Feedback is typically provided through recruiters, especially after technical or final rounds. While detailed technical feedback may be limited, you can expect high-level insights on your performance and fit for the role. thatgamecompany values transparency and often shares feedback to help candidates grow.

5.8 What is the acceptance rate for thatgamecompany Data Scientist applicants?
While specific rates aren’t published, the Data Scientist role at thatgamecompany is highly competitive, reflecting the studio’s reputation and the specialized nature of gaming analytics. Acceptance rates are estimated to be in the 3–5% range for well-qualified applicants.

5.9 Does thatgamecompany hire remote Data Scientist positions?
Yes, thatgamecompany offers remote opportunities for Data Scientists, with flexibility depending on team needs and project requirements. Some roles may require occasional in-person collaboration or travel for key meetings, but remote work is increasingly supported to attract global talent passionate about gaming and data science.

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