ZestyAI Data Scientist interview questions & guide 2026

The following questions reflect patterns from real interview experiences at ZestyAI. While specific tasks vary based on the team's current focus, the core themes center on your ability to apply data science to practical, often ambiguous, business scenarios.

Technical and SQL Data Manipulation

These questions test your fluency in data wrangling and your ability to extract insights from raw datasets using standard industry tools.

• Write a query using SQL window functions to calculate rolling averages for property risk scores over time.
• How would you handle a join between these two tables to identify missing data points in our climate feature set?

Preparation for ZestyAI requires a balance of hands-on coding, statistical theory, and business-focused problem solving. Do not rely solely on theoretical knowledge; you must be able to justify your choices with clear, data-driven reasoning.

Role-related knowledge – You must be proficient in the technical stack, specifically SQL and machine learning libraries like scikit-learn or pandas. Interviewers will evaluate your ability to write clean, efficient code and your depth of understanding regarding model lifecycle management.

Problem-solving ability – You will be presented with open-ended scenarios that lack a single "right" answer. Focus on how you structure your approach: define the problem, identify the metrics, propose a methodology, and acknowledge the potential limitations or risks.

Leadership and Communication – At ZestyAI, you are expected to influence outcomes. Be ready to discuss how you have managed stakeholder expectations, navigated project ambiguity, and contributed to team culture during past experiences.

The hiring process at ZestyAI is designed to be thorough yet efficient. It typically begins with an initial screening to gauge your background and cultural alignment. Following this, you will likely encounter a take-home assessment, which serves as a deep dive into your technical and analytical capabilities. Expect to spend a significant amount of time on this task; it is the cornerstone of the technical evaluation.

If you pass the assessment, you will move into a series of technical and product-focused interviews. These rounds often involve walking through your take-home work, live coding challenges, and case studies with cross-functional partners like product managers. The process is intended to be a two-way street, giving you ample opportunity to learn about the team and culture while they assess your fit for the role.

The timeline shows the typical progression from an initial screen to the final behavioral rounds. Use this structure to pace your preparation, ensuring you are ready for both the technical depth of the take-home assessment and the broader, high-level discussions in the later stages.

Experimentation and Statistics

This is a core competency for Data Scientist roles here. You must demonstrate that you understand not just how to run a test, but how to ensure its validity.

• A/B Testing – Focus on power analysis, randomization, and sample size requirements.
• Experimentation Pitfalls – Be prepared to discuss selection bias, novelty effects, and the dangers of "peeking" at data.
• Statistical Significance – Understand p-values, confidence intervals, and how to interpret them in a business context.

As a Data Scientist, your work will revolve around building, maintaining, and improving the predictive models that drive ZestyAI's business. You will spend a significant portion of your time on data extraction and cleaning, ensuring that the inputs for your models are robust. Collaboration is frequent; you will work closely with the engineering team to deploy models into production and with the product team to interpret the results and refine user-facing metrics.

Beyond core modeling, you will be expected to act as a data consultant for the business. This means performing ad-hoc analysis to answer specific questions from leadership, conducting experiments to test new product features, and proactively identifying opportunities for model optimization. Your success is measured by your ability to deliver reliable, scalable insights that directly support the company's risk assessment capabilities.

A successful candidate for this position brings a combination of strong analytical rigor and a pragmatic approach to problem-solving.

• Must-have skills:
  • Proficiency in SQL, particularly advanced query writing.
  • Strong foundation in statistics and experimental design.
  • Experience with machine learning frameworks (e.g., Python, scikit-learn, pandas).
  • Ability to communicate complex technical findings to non-technical stakeholders.
• Nice-to-have skills:
  • Experience with geospatial data or property-related datasets.
  • Familiarity with cloud-based data warehouses.
  • Background in the insurance or fintech industries.

Q: How much time should I dedicate to the take-home assignment? A: Treat it as a high-priority task. While the company may suggest not overthinking it, successful candidates often provide a well-documented, thoughtful analysis that demonstrates their full technical potential.

Q: Is the technical interview very difficult? A: The technical questions are generally practical rather than theoretical. If you have a solid grasp of SQL and basic machine learning, you should be well-prepared for the technical rounds.

Q: What is the company culture like? A: ZestyAI values autonomy, self-care, and a collaborative environment. They look for people who are proactive and genuinely interested in the impact of their work on the insurance industry.

Q: How long does the entire process take? A: The process is generally fast and efficient, often moving from the initial screen to an offer within a few weeks, provided you are a strong fit.

• Structure your answers: When answering case studies, use a framework like the CIRCLES method to ensure you cover the problem, the goal, and the solution in a logical order.
• Master your resume: Be ready to explain every project listed, specifically the "why" behind your technical choices and the impact of the final result.
• Ask thoughtful questions: At the end of your interviews, ask about current challenges the team is facing or how they balance innovation with production stability; this shows genuine engagement.
• Prepare for ambiguity: Many interview questions will be open-ended; take a moment to clarify the goal with the interviewer before jumping into a solution.

The Data Scientist role at ZestyAI is an exceptional opportunity to apply advanced analytics to high-stakes, real-world problems. By mastering the core technical requirements—specifically SQL and experimental design—and demonstrating a strong product mindset, you will position yourself as a top-tier candidate. Remember that your ability to communicate your thought process is just as important as your technical output.

You can explore additional interview insights, practice questions, and preparation resources on Dataford to sharpen your skills further. Stay focused, remain analytical, and trust in your preparation. You have the potential to make a significant impact at ZestyAI.

The compensation data provided above reflects the current market range for the Data Scientist role in major Canadian hubs. These figures are base salary ranges; understand that total compensation may include additional benefits, equity, or performance-based incentives depending on your seniority and specific offer details.