What is a Data Analyst at Tatari?

As a Data Analyst (often titled internally as a Data Science Analyst) at Tatari, you are stepping into a pivotal role at the intersection of advertising technology, advanced analytics, and client strategy. Tatari is revolutionizing how brands measure and buy television advertising across both linear and streaming platforms. In this role, you are not just querying databases; you are building the analytical foundation that proves the return on investment for massive ad campaigns.

Your impact extends directly to both the product and the clients. By transforming complex viewership and conversion data into actionable insights, you empower brands to optimize their TV spend with the same precision they expect from digital marketing. You will work closely with data engineering, client success, and product teams to refine measurement methodologies, build predictive models, and scale reporting capabilities.

What makes this position uniquely challenging and exciting is the sheer scale and complexity of the data. You will navigate fragmented media landscapes, probabilistic matching, and incrementality testing. If you thrive in a fast-paced environment where your analytical rigor directly influences high-stakes business decisions, this role will offer you immense strategic influence and continuous learning.

Common Interview Questions

Getting Ready for Your Interviews

Preparing for an interview at Tatari requires a balanced approach. You must demonstrate deep technical fluency while simultaneously proving your ability to translate complex data into clear business strategies. Your interviewers will evaluate you across several core dimensions.

Technical Fluency and Data Manipulation – This measures your core hard skills, specifically in SQL and Python. Interviewers will look at how efficiently you can extract, clean, and manipulate large datasets, and whether you write scalable, readable code. You can demonstrate strength here by practicing complex joins, window functions, and data transformations under time constraints.

Statistical Rigor and Experimentation – Measurement is the lifeblood of Tatari. This criterion evaluates your understanding of hypothesis testing, causal inference, and A/B testing. Strong candidates will confidently discuss incrementality, statistical significance, and how to handle noisy or incomplete data sets in a real-world advertising context.

Business Acumen and Problem-Solving – You will be tested on how you approach ambiguous business challenges. Interviewers want to see how you structure a problem, identify the right metrics to track, and tie your analytical findings back to the client's return on ad spend (ROAS). You can excel here by thinking aloud and always connecting data points to business outcomes.

Communication and Culture Fit – Tatari values collaboration, agility, and clear communication. You will be evaluated on your ability to explain complex technical concepts to non-technical stakeholders. Strong candidates demonstrate a proactive, ownership-driven mindset and a collaborative approach to cross-functional teamwork.

Interview Process Overview

The interview process for a Data Science Analyst at Tatari is rigorous, data-centric, and designed to mirror the actual challenges you will face on the job. Expect a fast-paced progression that quickly moves from high-level background discussions into deep technical evaluations. The company places a heavy emphasis on your ability to handle real-world data, meaning the technical assessments are highly practical rather than purely academic.

Typically, the process begins with a recruiter screen to align on your background, expectations, and interest in AdTech. This is followed by a hiring manager interview that digs into your past projects and problem-solving methodology. A defining feature of the Tatari process is the technical assessment—often a take-home data challenge or a live technical screen—where you will analyze a dataset, draw conclusions, and present your findings. The final stage is a comprehensive virtual onsite loop covering coding, statistical concepts, and behavioral alignment.

Tatari distinguishes itself by focusing heavily on how you communicate your results. You will not only be judged on whether your code compiles or your math is correct, but on how effectively you synthesize that information into a narrative that a client or product manager could understand.

The visual timeline above outlines the typical stages of the Tatari interview loop, moving from initial screens through technical assessments and the final onsite panel. Use this to pace your preparation—focus first on refreshing your core SQL and Python skills for the early technical rounds, and reserve time later to practice your presentation and case study communication for the final interviews.

Deep Dive into Evaluation Areas

To succeed, you need to deeply understand the core competencies Tatari targets during the interview loop. The evaluation is highly practical, focusing on skills you will use every day.

SQL and Data Engineering Fundamentals

As a Data Science Analyst, you will spend a significant amount of time extracting and structuring data. This area evaluates your ability to write highly efficient, accurate SQL queries to handle large, complex datasets typical of TV viewership and digital attribution. Strong performance means writing clean, optimized code without needing excessive hints.

Be ready to go over:

• Advanced Joins and Aggregations – Understanding how to merge massive tables without duplicating records or creating Cartesian products.
• Window Functions – Using RANK(), LEAD(), LAG(), and rolling averages to analyze time-series data, such as tracking user conversions after an ad airs.
• Data Cleaning and Formatting – Handling nulls, casting data types, and using common table expressions (CTEs) to structure complex logic.
• Advanced concepts (less common) – Query execution plans, indexing strategies, and basic database architecture.

Example questions or scenarios:

• "Given a table of ad airings and a table of website visits, write a query to find the number of visits that occurred within 5 minutes of an ad airing."
• "How would you write a query to identify the top three performing networks by ROAS over a rolling 30-day period?"
• "Explain how you would handle duplicate records in a daily viewership log."

Statistics and Measurement Methodology

Because Tatari provides granular TV measurement, your grasp of statistics must be rock solid. This area tests your ability to design experiments, measure incrementality, and understand the mathematical principles behind attribution. A strong candidate will know the difference between correlation and causation and can explain statistical concepts in plain English.

Be ready to go over:

• A/B Testing and Experimentation – Designing holdout tests, determining sample sizes, and calculating statistical significance.
• Causal Inference – Understanding how to measure the true incremental lift of an ad campaign when a perfect control group isn't available.
• Probability Distributions – Familiarity with normal, binomial, and Poisson distributions in the context of user behavior.
• Advanced concepts (less common) – Propensity score matching, synthetic control methods, and regression analysis for attribution.

Example questions or scenarios:

• "A client claims their recent spike in sales is entirely due to a new TV campaign. How would you statistically prove or disprove this?"
• "Explain the concept of statistical power and why it matters when setting up an incrementality test for a smaller brand."
• "How would you handle a situation where your A/B test results are skewed by a major external event, like a holiday?"

Product and Business Case Studies

Your technical skills are only valuable if they solve business problems. This area evaluates your AdTech domain knowledge and your ability to translate data into strategic recommendations. Interviewers want to see a structured thought process and a deep focus on the client's ultimate goals.

Be ready to go over:

• Metric Definition – Identifying the right KPIs for a given business objective (e.g., Cost Per Acquisition, Return on Ad Spend, Conversion Rate).
• Attribution Modeling – Understanding the nuances of first-touch, last-touch, and multi-touch attribution, especially bridging TV and digital.
• Root Cause Analysis – Investigating sudden drops or spikes in performance metrics.
• Advanced concepts (less common) – Media mix modeling (MMM) and cross-device graph matching.

Example questions or scenarios:

• "Walk me through how you would investigate a sudden 20% drop in overall conversion rate for a major e-commerce client."
• "If a client wants to shift budget from streaming back to linear TV, what data points would you pull to advise them?"
• "How do you define a 'conversion' when measuring the impact of a brand-awareness TV commercial?"