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.

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?"

Key Responsibilities

As a Data Science Analyst at Tatari, your day-to-day work is a dynamic mix of deep analytical execution and strategic collaboration. Your primary responsibility is to leverage Tatari's proprietary data infrastructure to measure the effectiveness of TV advertising campaigns. You will spend a large portion of your time querying massive datasets—combining ad airing logs with client conversion data—to calculate metrics like cost-per-acquisition (CPA) and return on ad spend (ROAS).

Beyond standard reporting, you will drive advanced analytical projects. This involves designing and executing incrementality tests, building predictive models to forecast campaign performance, and developing automated dashboards using tools like Tableau or Looker. You are expected to continuously refine the methodologies used to match TV viewers to digital actions, navigating the complexities of probabilistic data and cross-device behavior.

Collaboration is central to this role. You will partner closely with the Data Engineering team to ensure data pipelines are robust and accurate. Simultaneously, you will act as a strategic advisor to the Client Success and Media Buying teams. When a campaign underperforms, they will look to you for root-cause analysis and data-backed recommendations on how to reallocate spend. You are not just providing numbers; you are providing the narrative that drives the client's advertising strategy.

Role Requirements & Qualifications

To be highly competitive for the Data Science Analyst position at Tatari, you need a strong blend of technical proficiency, statistical knowledge, and business intuition. The ideal candidate typically brings 2 to 4 years of experience in an analytical role, preferably within AdTech, media, or a fast-paced technology company.

• Must-have technical skills – Expert-level SQL is non-negotiable; you must be able to write complex, optimized queries from scratch. Proficiency in Python (specifically Pandas, NumPy) for data manipulation and basic modeling is required. You also need strong experience with data visualization tools (e.g., Tableau, Looker) to build scalable dashboards.
• Must-have analytical skills – A deep understanding of applied statistics, specifically A/B testing, hypothesis testing, and incrementality measurement. You must be comfortable working with large, messy, and probabilistic datasets.
• Nice-to-have skills – Prior experience with media mix modeling (MMM), familiarity with Snowflake or AWS environments, and direct experience in TV advertising measurement or digital attribution.
• Soft skills – Exceptional communication skills are critical. You must be able to translate complex statistical concepts into actionable business insights for non-technical stakeholders and clients. A proactive, ownership-driven mindset is essential for navigating the ambiguity of a high-growth environment.

Common Interview Questions

The questions below represent the types of challenges you will face during the Tatari interview loop. They are designed to test not just your ability to find an answer, but your methodology, code efficiency, and business logic. Use these to identify patterns in how Tatari evaluates candidates.

SQL and Data Processing

These questions test your ability to manipulate complex, realistic datasets efficiently.

• Write a SQL query to calculate the 7-day rolling average of daily conversions per ad network.
• Given a table of user sessions and a table of purchases, how would you find the conversion rate of users who visited the site within 10 minutes of an ad airing?
• Explain how you would optimize a query that is taking too long to run on a table with billions of rows.
• Write a query to find the top 5 campaigns that generated the highest ROAS, filtering out campaigns with less than $10,000 in spend.
• How do you handle missing or delayed data in a daily reporting pipeline?

Statistics and Experimentation

These questions evaluate your grasp of the mathematical principles behind ad measurement.

• How would you explain a p-value to a client who has no background in statistics?
• Walk me through how you would design an incrementality test for a brand running ads on a specific streaming platform.
• What are the risks of calling an A/B test early, and how do you mitigate them?
• If a client's control group accidentally receives exposure to the ad, how would you adjust your analysis?
• Explain the difference between correlation and causation using an example from advertising.

Business and AdTech Case Studies

These questions assess your domain knowledge and ability to drive business strategy.

• A client's CPA (Cost Per Acquisition) has suddenly spiked by 30% week-over-week. Walk me through your troubleshooting steps.
• How would you attribute a website purchase to a TV ad when you only have IP-level matching?
• If a client wants to maximize brand awareness rather than direct conversions, what metrics would you propose tracking?
• Describe a time you had to push back on a stakeholder's request because the data did not support their hypothesis.
• How do you balance the trade-off between a highly accurate attribution model and one that is easy for a client to understand?

Frequently Asked Questions

Q: How difficult is the technical assessment for this role? The technical assessment is rigorous but fair. It is deeply grounded in the actual day-to-day work at Tatari, meaning you won't face abstract algorithmic brainteasers. Expect complex SQL joins, data cleaning in Python, and a strong emphasis on drawing business insights from the data you process.

Q: Do I need prior experience in TV advertising or AdTech? While prior AdTech experience is a significant advantage, it is not strictly required if you have a strong foundation in analytics and statistics. However, you should thoroughly research industry concepts like ROAS, incrementality, linear vs. streaming TV, and probabilistic attribution before your interviews.

Q: What differentiates a good candidate from a great candidate at Tatari? A good candidate writes clean code and gets the math right. A great candidate does that, but also proactively identifies what the data means for the business. Great candidates communicate their findings as a compelling story, highlighting actionable recommendations rather than just presenting raw numbers.

Q: What is the typical timeline from the first screen to an offer? The process usually takes between 3 to 5 weeks. Tatari moves efficiently, but the inclusion of a technical assessment or take-home challenge requires dedicated time from the candidate. Recruiters are generally communicative and transparent about timelines.

Q: What is the culture like for the Data team in the New York office? The environment is highly collaborative, fast-paced, and intellectually stimulating. Because the company sits at the intersection of tech and media, the culture blends engineering rigor with client-focused agility. Expect to be given significant ownership over your projects early on.

Other General Tips

• Clarify before you code: When given a SQL or Python problem, do not start typing immediately. Take a moment to ask clarifying questions about data types, edge cases (like null values or duplicates), and the ultimate business goal of the query.
• Master the AdTech vocabulary: Ensure you are completely comfortable discussing metrics like CPA, ROAS, CPM, and conversion rates. Using the correct terminology naturally during your interviews demonstrates domain readiness.

• Structure your case study answers: Use frameworks like STAR (Situation, Task, Action, Result) for behavioral questions, and structured logical trees for root-cause analysis. Interviewers at Tatari highly value organized thinking.
• Practice communicating statistics simply: You will frequently interact with non-technical stakeholders. Practice explaining complex concepts like statistical significance or causal inference using simple, everyday analogies.

Summary & Next Steps

Securing a Data Analyst or Data Science Analyst role at Tatari is a fantastic opportunity to position yourself at the cutting edge of advertising technology. You will be tackling complex, large-scale data challenges that have a direct, measurable impact on how major brands allocate millions of dollars in media spend. The role demands a unique hybrid of deep technical execution, statistical rigor, and sharp business acumen.

The compensation data above provides a baseline expectation for this role in the New York market. Keep in mind that total compensation often includes a mix of base salary, performance bonuses, and equity, which can vary based on your specific experience level and performance during the interview loop.

To succeed, focus your preparation on mastering advanced SQL, refining your understanding of experiment design, and practicing how to communicate data-driven narratives clearly. Remember that your interviewers want you to succeed; they are looking for a collaborative problem-solver who can navigate ambiguity with confidence. Continue exploring specific technical deep-dives and mock interview scenarios on Dataford to sharpen your skills. Approach this process with curiosity and confidence, and you will be well-prepared to demonstrate your full potential.