At Lg Ad Solutions, a Data Analyst plays a pivotal role in shaping the future of Connected TV (CTV) advertising. As television transitions from traditional broadcast to internet-streaming environments, the volume, velocity, and complexity of viewing and ad-interaction data have grown exponentially. This role sits at the intersection of media, technology, and data science, transforming massive streams of automated content recognition (ACR) and ad delivery data into actionable insights for advertisers and internal product teams.

You will be responsible for analyzing campaign performance, measuring audience attribution, and finding optimization opportunities across millions of screens. Your work directly impacts how advertisers target their audiences, measure their return on investment, and optimize their ad spend. By providing deep-dive analyses and building robust reporting frameworks, you will help Lg Ad Solutions deliver high-impact, privacy-first advertising experiences to consumers globally.

This position requires a unique blend of technical execution, quantitative rigor, and business acumen. You will not just be querying databases; you will be translating complex data patterns into strategic narratives. Whether you are working with data engineers to optimize data pipelines or collaborating with product managers to evaluate new targeting algorithms, your analytical contributions will drive the core business engine.

The following questions are representative of what you can expect during the hiring process. They are drawn from real interview experiences of candidates who have interviewed for the Data Analyst position at Lg Ad Solutions. Use these questions to identify patterns in the evaluation process rather than simply memorizing answers.

Quantitative & Statistical Analysis

These questions assess your foundational understanding of probability, statistics, and your ability to work with quantitative data under pressure.

• How would you design an A/B test to measure the effectiveness of a new ad placement on a smart TV home screen?
• Explain the difference between correlation and causation in the context of ad impressions and conversion rates.

To succeed in the Lg Ad Solutions interview process, you must demonstrate a balanced skill set that spans technical mastery, quantitative reasoning, and business communication. The hiring team looks for analysts who do not just run queries but who can also connect their findings to broader business objectives.

Technical & Domain Knowledge – You must show a deep understanding of data manipulation tools, specifically SQL and Python or R. Be ready to explain your choice of analytical methodologies and how you ensure data quality and query efficiency when working with large datasets.

Structured Problem-Solving – Interviewers will evaluate how you approach complex, ambiguous problems. Whether solving a guesstimate or analyzing a business case, you should break the problem down into logical components, state your assumptions clearly, and walk the interviewer through your framework step-by-step.

Product & Business Acumen – You need to understand the ad tech ecosystem, particularly Connected TV (CTV), programmatic advertising, and digital media metrics. Showing that you understand how Lg Ad Solutions generates value will immediately set you apart.

Communication & Influence – Data is only valuable if it can be understood and acted upon. You must be able to translate complex statistical and technical findings into clear, concise, and actionable recommendations for business stakeholders and product teams.

The interview process for a Data Analyst at Lg Ad Solutions is designed to thoroughly evaluate your technical capability, analytical mindset, and cultural alignment. Candidates typically go through a multi-stage process that balances take-home assessments with live technical and behavioral discussions.

The journey usually begins with a resume screening, which may be followed by a take-home case study or written assessment. This initial phase is highly focused on practical execution—allowing you to showcase your data cleaning, analysis, and reporting skills on a realistic dataset. Following a successful submission, you will transition to live interview rounds. These rounds are highly collaborative but rigorous, featuring a mix of live technical problem-solving, quantitative tests, business case discussions, and behavioral interviews.

The timeline above outlines the typical progression from your initial application to the final decision. Candidates should use this sequence to pace their preparation, focusing heavily on hands-on data manipulation in the early stages and shifting toward business cases, guesstimates, and behavioral storytelling as they progress to the live rounds. Note that the exact number of live rounds may vary slightly based on the seniority of the position and the specific team.

Take-Home Case Study & Technical Assessment

The take-home case study is a critical gatekeeper in the Lg Ad Solutions hiring process. It simulates the actual work of a Data Analyst by providing you with a raw dataset and asking you to perform an end-to-end analysis.

Be ready to go over:

• Data Cleaning & Structuring – Handling missing values, anomalies, and formatting raw data for analysis.
• Exploratory Data Analysis (EDA) – Identifying trends, correlations, and key drivers within the provided dataset.
• Insights Delivery – Translating your analytical findings into a clean, professional, and structured business report or slide deck.
• Advanced concepts (less common) – Applying basic predictive modeling or cohort analysis to project future campaign performance.

Example scenarios:

• "Analyze a 30-day campaign dataset and identify which ad creatives and targeting strategies yielded the highest return on ad spend (ROAS)."
• "Determine the primary causes of ad inventory underutilization based on programmatic bidding logs."

Quantitative Analysis, Guesstimates & Statistics

During the live analytical rounds, you will face questions designed to test your mathematical intuition, statistical foundation, and ability to think on your feet.

Be ready to go over:

• Statistical Significance – Understanding p-values, sample sizes, and statistical power in the context of A/B testing.
• Guesstimates – Structuring top-down or bottom-up estimation frameworks for ambiguous market-sizing questions.
• Probability & Metrics – Calculating and interpreting metrics like click-through rates, conversion rates, and lifetime value.

Example scenarios:

• "Estimate the total number of ad impressions served daily on smart TVs in India."
• "Explain how you would determine if a 2% increase in ad engagement in a test group is statistically significant."

Coding, SQL & Logic

Your technical rounds will evaluate your ability to write clean, efficient code and explain your programmatic logic. You may encounter a mix of live SQL writing, pseudo-code exercises, or basic Python programming.

Be ready to go over:

• SQL Joins & Aggregations – Master complex joins (LEFT, INNER, FULL), window functions (ROW_NUMBER, RANK), and aggregations (GROUP BY, HAVING).
• Logical Pseudo-code – Writing structured, language-agnostic logic to solve data manipulation puzzles.
• Query Optimization – Understanding how index usage, subqueries, and partition filtering affect query performance on large datasets.

Example scenarios:

• "Write a SQL query to identify the top 5 performing ad campaigns by revenue for each month over the past year."
• "Draft pseudo-code to parse a nested JSON string containing user device interactions and flatten it into a structured schema."