Teya Data Scientist interview questions & guide 2026

Our interview process is designed to evaluate your ability to apply data science principles to real-world business scenarios. While specific questions may vary by team, you should prepare for a mix of technical proficiency and product-focused problem solving.

Product-Sense

These questions test your ability to think like a product owner. You must demonstrate that you understand how data influences user experience and business outcomes.

• How would you design a new metric to measure the success of our payment gateway?
• If our core conversion rate dropped by 5% overnight, how would you go about diagnosing the root cause?

Preparation at Teya requires a balance of technical precision and business context. You should not only be ready to write code but also to articulate the "why" behind your technical choices.

Technical Proficiency – You must be comfortable with the entire data stack, from raw data extraction to model deployment. Interviewers will look for clean, efficient code and a deep understanding of statistical foundations.

Product Intuition – Success here depends on your ability to link data to product goals. Practice connecting technical metrics to business outcomes, demonstrating that you understand how your analysis impacts the bottom line.

Communication and Influence – You will often be the bridge between technical and non-technical teams. Focus on your ability to simplify complex concepts and drive consensus among stakeholders.

Ownership and Adaptability – We value candidates who take initiative. Be prepared to discuss how you have navigated ambiguity in past roles and taken projects from conception to implementation.

The interview journey at Teya is designed to provide a comprehensive view of your skills across technical, behavioral, and product dimensions. While the process can be demanding, it is structured to ensure that you meet the various stakeholders you will work with daily. Expect a mix of virtual interviews that cover your technical background, your ability to tackle data problems, and your cultural fit within the organization.

The visual timeline above illustrates the typical progression from initial screening through to the final round. Candidates should use this as a roadmap to manage their preparation energy, ensuring they are equally ready for the technical deep dives and the conversational, leadership-focused rounds. Please note that while this represents our standard flow, individual team needs may lead to slight variations in the order or specific focus of certain rounds.

Data Manipulation and SQL

We prioritize candidates who can handle real-world data complexity. You should be proficient in writing performant queries and using advanced functions to derive insights.

• Window functions – Essential for time-series analysis and cohort tracking.
• Data cleaning – How you handle missing values and outliers in production datasets.
• Optimization – Understanding indexing and query execution plans.

As a Data Scientist, your day-to-day involves transforming data into strategy. You will spend significant time collaborating with product squads to define success metrics for new features and monitoring existing ones for stability. A core part of your responsibility is designing and analyzing experiments, ensuring that every product change is backed by rigorous evidence.

Beyond experimentation, you will maintain and improve the data pipelines that power our decision-making. You will work closely with data engineers to ensure data quality and with product managers to translate high-level business questions into concrete analytical tasks. Your goal is to provide the team with the clarity needed to iterate quickly and confidently.

We look for Data Scientists who are as comfortable talking to product managers as they are writing complex SQL.

• Must-have skills:
  • Advanced proficiency in SQL (including window functions).
  • Strong foundation in A/B testing design and statistical analysis.
  • Experience with product metric design and diagnostic analysis.
  • Ability to communicate technical findings to non-technical stakeholders.
• Nice-to-have skills:
  • Experience with machine learning model deployment in production.
  • Familiarity with cloud-based data warehouses.
  • Experience in the fintech or payments domain.

Q: How difficult is the interview process? The process is rigorous but fair. We focus on practical application rather than theoretical trivia, so expect to solve problems that mirror the work you would actually do here.

Q: How much time should I spend preparing? Candidates who succeed typically spend several weeks brushing up on statistical concepts, practicing SQL, and reviewing their past projects to ensure they can speak to their impact clearly.

Q: Is the process remote-friendly? Yes, our interview process is designed to be conducted virtually to accommodate candidates from various locations.

Q: What differentiates a successful candidate? Successful candidates distinguish themselves by showing deep curiosity about our products and a clear ability to tie their technical solutions to business value.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses concise and impact-focused.
• Think aloud: When solving coding or case study problems, explain your thought process. We are more interested in your approach than just the final answer.
• Ask clarifying questions: In case studies, we appreciate candidates who ask about the business context before jumping into a solution.

• Focus on the "why": Whenever you mention a technical choice, be prepared to explain why that was the best approach given the constraints.

Joining Teya as a Data Scientist offers a unique opportunity to shape the future of merchant payments through data-backed innovation. By focusing on your ability to design experiments, diagnose metric shifts, and communicate effectively across teams, you will be well-positioned to demonstrate your value throughout the interview process. We encourage you to explore additional interview insights, practice questions, and preparation resources on Dataford to further refine your skills.

The compensation data provided reflects the current market standards for this position. Candidates should interpret these ranges as a baseline, considering that final offers are adjusted based on individual experience, seniority, and specific team requirements. We look forward to seeing how your background and expertise can contribute to our mission.