What is a Research Scientist at Yelp?

As a Research Scientist (often referred to internally as an Applied Scientist) at Yelp, you are not just analyzing data in a vacuum; you are the engine behind the intelligence that connects millions of users with great local businesses. This role sits at the intersection of rigorous academic research and practical product engineering. You are responsible for building the algorithms that power Search, Recommendations, Ads, and Trust & Safety.

The impact of this role is highly visible. When a user searches for "best sushi near me," the ranking model you optimize determines what they see. When a business owner looks at their ad performance, your predictive models drive that efficiency. You will work on complex challenges involving Natural Language Processing (NLP) to understand review sentiment, Computer Vision to analyze user photos, and Graph Learning to map user-business interactions.

Unlike pure academic research roles, Yelp prioritizes applied science. This means you will own the full lifecycle of your models—from ideation and prototyping to offline evaluation, A/B testing, and productionization. You will work in a collaborative environment where the goal is to ship code that improves the user experience, making this an ideal role for scientists who want to see their work deployed at scale.

Getting Ready for Your Interviews

Preparing for the Research Scientist interview requires a shift in mindset. You need to demonstrate that you can bridge the gap between theoretical correctness and practical application. Yelp looks for candidates who can take a vague business problem and translate it into a concrete machine learning solution.

Your interviewers will evaluate you based on the following key criteria:

Applied Machine Learning This is the core of the evaluation. You must demonstrate a deep intuition for selecting the right models for specific problems (e.g., Ranking vs. Classification). Interviewers assess your ability to handle real-world messy data, feature engineering, and the trade-offs between model complexity and inference latency.

Coding and Implementation While you are a scientist, you are expected to write production-quality code. Evaluation focuses on your proficiency in Python and data manipulation libraries (like Pandas or NumPy), as well as your ability to write clean, efficient algorithms. You will likely face coding questions that require you to implement ML concepts or data structures from scratch.

Product Sense & Problem Solving Yelp values scientists who understand the "why" behind the "how." You will be evaluated on your ability to define success metrics (e.g., CTR, conversion rate, dwell time) and design experimental frameworks (A/B testing) to validate your hypotheses.

Culture Fit & Communication Yelp prides itself on a culture that is often described as friendly, authentic, and collaborative. Interviewers look for candidates who can explain complex technical concepts to non-experts and who embody the company’s values, such as "Be Authentic" and "Play Well With Others."

Interview Process Overview

The interview process for a Research Scientist at Yelp is rigorous but structured to be transparent and respectful of your time. It typically begins with a recruiter screen to discuss your background and interest in the role. This is followed by a technical screen, which usually involves a mix of coding and basic machine learning theory. If you pass this stage, you will move to the virtual onsite loop.

The onsite loop is comprehensive, consisting of 4–5 separate rounds. You can expect a deep dive into your past research or projects, a dedicated Machine Learning System Design round, a coding round focused on algorithms or data manipulation, and a behavioral round focused on Yelp’s core values. The atmosphere is generally described by candidates as warm and supportive; interviewers want you to succeed and will often provide hints if you get stuck.

Unlike some big-tech companies that rely heavily on standardized, silent testing, Yelp emphasizes dialogue. You are expected to "think out loud" throughout the process. The leveling for the role (e.g., Senior vs. Mid-level) is often determined based on your performance during these interviews rather than being fixed beforehand.

The timeline above illustrates the typical progression from application to offer. Use this to plan your preparation: the early stages validate your baseline skills, while the onsite demands deep endurance and the ability to switch contexts between coding, high-level design, and behavioral questions.

Deep Dive into Evaluation Areas

To succeed, you must demonstrate mastery across several distinct domains. Based on candidate reports, the following areas are critical for the Research Scientist track.

Machine Learning Theory & Breadth

This area tests your academic foundation. You shouldn't just know how to use a library; you need to understand the mathematics underneath.

Be ready to go over:

• Supervised Learning – Deep understanding of Regression, SVMs, Random Forests, and Gradient Boosting (XGBoost/LightGBM).
• Deep Learning – Architecture specifics for CNNs (if vision-focused), RNNs/LSTMs, and Transformers (BERT, GPT).
• Evaluation Metrics – Precision, Recall, F1-score, ROC-AUC, and specifically ranking metrics like NDCG and MAP.
• Optimization – How Gradient Descent works, different optimizers (Adam, SGD), and regularization techniques (L1/L2, Dropout).

Example questions or scenarios:

• "Explain the bias-variance tradeoff to a non-technical person."
• "How does a Random Forest decide where to split a node?"
• "Derive the loss function for Logistic Regression."

ML System Design (Applied Modeling)

This is often the "make or break" round. You will be given an open-ended product problem and asked to design an end-to-end ML system.

Be ready to go over:

• Problem Formulation – Translating a product feature (e.g., "Recommended for You") into a prediction task.
• Feature Engineering – Handling categorical data, embeddings, user history, and context features.
• Training & Serving – Online vs. offline training, handling data drift, and serving constraints.
• Advanced concepts – Multi-task learning, cold-start problems, and handling class imbalance.

Example questions or scenarios:

• "Design a restaurant recommendation system for a user visiting a new city."
• "How would you build a model to detect fake reviews on Yelp?"
• "Design a search ranking algorithm for local businesses."

Coding & Data Structures

Expect practical coding questions. While you should know basic algorithms, Yelp often leans toward questions that mimic daily data science work.

Be ready to go over:

• Data Manipulation – Using Python to parse logs, aggregate data, or clean datasets (often without Pandas, or using standard libraries).
• Algorithms – Arrays, Strings, Hash Maps, and Sliding Windows.
• Probability & Statistics – Writing code to simulate a probability problem or compute statistical significance.

Example questions or scenarios:

• "Given a stream of user check-ins, find the top K most popular businesses in the last hour."
• "Implement a function to calculate the Moving Average of a data stream."
• "Write a script to parse a text file and count word frequencies excluding stop words."

Key Responsibilities

As a Research Scientist, your daily work will revolve around improving the core intelligence of Yelp's platform. You will spend a significant portion of your time exploring data to find patterns and opportunities. This involves writing complex SQL queries and using Python notebooks to prototype new ideas.

Once a prototype shows promise, you will be responsible for productionizing it. This is a key differentiator at Yelp: you don't just hand off a model to an engineer. You work within the production codebase to deploy your models, requiring you to understand software engineering best practices. You will collaborate closely with Product Managers to define the scope of projects and with Backend Engineers to ensure your models scale to handle millions of requests.

You will also design and monitor A/B tests. You must interpret the results of these experiments to decide whether to launch a new model or iterate further. Whether you are working on Ads quality, Search relevance, or Photo classification, your work directly impacts revenue and user satisfaction.

Role Requirements & Qualifications

Yelp seeks candidates who have a strong blend of academic rigor and engineering capability.

• Must-have Technical Skills:

  • Proficiency in Python is non-negotiable.
  • Strong command of SQL for data extraction.
  • Experience with ML frameworks like PyTorch, TensorFlow, or Scikit-learn.
  • Solid understanding of probability, statistics, and linear algebra.

• Experience Level:

  • Typically requires a Master’s or PhD in Computer Science, Statistics, Mathematics, or a related field.
  • For candidates without a PhD, substantial industry experience in training and deploying ML models is required.
  • Experience with large-scale distributed systems (e.g., Spark, Hadoop) is highly valued.

• Soft Skills:

  • Ability to communicate technical results to non-technical stakeholders.
  • A collaborative mindset; Yelp values team players over "brilliant jerks."
  • Curiosity and a user-first mentality.

Common Interview Questions

The following questions are representative of what you might face. They are drawn from reported candidate experiences for the Research Scientist and Applied Scientist roles at Yelp. Do not memorize answers; use these to identify the types of problems you need to be comfortable solving.

Machine Learning & Statistics

These questions test your fundamental knowledge and ability to derive concepts.

• "What is the difference between Bagging and Boosting?"
• "How do you handle missing values in a dataset? What are the pros and cons of imputation?"
• "Explain the concept of p-value and statistical power."
• "Why might you choose a decision tree over a neural network for a specific problem?"
• "How does L1 regularization result in sparse models compared to L2?"

Coding & Algorithms

Expect questions that involve data structures or probability simulations.

• "Given a list of photo tags, group them by category."
• "Implement the K-Means clustering algorithm from scratch."
• "Find the median of two sorted arrays."
• "Write a function to sample from a discrete probability distribution."

Behavioral & Culture

Yelp takes these questions seriously to ensure alignment with their values.

• "Tell me about a time you disagreed with a product manager or stakeholder. How did you resolve it?"
• "Describe a project where you had to learn a new technology quickly."
• "Tell me about a time you made a mistake in your analysis. How did you handle it?"
• "Why do you want to work at Yelp specifically?"

Frequently Asked Questions

Q: Is the coding round LeetCode-style or practical? Most candidates report a mix, but with a lean toward practical application. You might get a standard algorithm question (Medium difficulty), but you are equally likely to get a data manipulation task that tests your ability to write clean Python code to process data.

Q: How does Yelp determine the level (e.g., Senior vs. Mid-level) for this role? Recruiters have explicitly noted that leveling is often fluid and decided based on your interview performance. A strong performance in the system design and depth rounds can bump you up to a Senior level consideration.

Q: What is the work-life balance like for Scientists at Yelp? Yelp is frequently rated highly for work-life balance compared to other tech giants. The culture is described as "friendly" and "human-centric," with reasonable hours and a supportive management style, though this can vary slightly by specific team.

Q: Can I work remotely? Yes, Yelp has adopted a "remote-first" philosophy for many roles. Most engineering and science teams are distributed, and the company has effective processes in place to support remote collaboration.

Q: How much domain knowledge (e.g., Ads, Search) do I need? While domain knowledge is a plus, Yelp hires generalist scientists. Strong fundamentals in ML theory and engineering are more important than knowing the specifics of Ad-tech or Search ranking beforehand, though you should be ready to apply your general knowledge to these domains during the interview.

Other General Tips

Know the Product Inside Out Download the Yelp app before your interview. Use it to find a restaurant or a plumber. Notice how the search results are ranked, how the ads are displayed, and how photos are organized. Being able to reference specific product behaviors during your System Design round shows initiative and product sense.

Brush Up on SQL Unlike some research roles that are purely Python-based, Yelp scientists often pull their own data. You may be asked to write SQL queries on a whiteboard or in a shared editor. Ensure you are comfortable with JOINs, GROUP BY, and window functions.

Communicate Your Thought Process In the coding and design rounds, silence is a red flag. If you are making an assumption (e.g., "I'm assuming the data fits in memory"), state it clearly. Interviewers at Yelp are collaborative; if you talk through your logic, they can guide you away from pitfalls.

Prepare for "Why Yelp?" This seems standard, but Yelp looks for genuine interest. Connect your answer to their specific challenges—local search is a unique problem space involving sparse data, geographic constraints, and high trust requirements.

Summary & Next Steps

The Research Scientist role at Yelp is a premier opportunity for those who want to apply high-level machine learning to tangible, human-centric problems. You will be working in a data-rich environment where your models help real people find great local businesses. The culture is supportive, the work-life balance is respected, and the technical challenges in NLP, Computer Vision, and Recommender Systems are world-class.

To succeed, focus your preparation on the intersection of theory and practice. Don't just memorize equations; understand how to implement them and how they drive product metrics. Be ready to code in Python, design scalable systems, and communicate your ideas with clarity and authenticity.

The salary data above provides a baseline for what you can expect. Note that total compensation at Yelp typically includes base salary, significant equity (RSUs), and a performance bonus. The specific offer will depend heavily on the level determined during your interview loop and your location.

You have the skills to excel in this process. approach the interviews as a conversation between colleagues, stay curious, and show them how your scientific mindset can drive value for Yelp's users. Good luck!

For more exclusive interview insights and resources, visit Dataford.