To succeed in the Moloco interview process, you must approach your preparation with a structured mindset. We evaluate candidates across several dimensions, and a strong performance requires demonstrating both deep technical expertise and strong collaborative skills.

Technical Rigor & Domain Knowledge – You must possess a flawless understanding of probability, statistics, and machine learning theory. Moloco interviewers value candidates who understand the mathematical "why" behind model behaviors, rather than those who simply import libraries and run default algorithms.

Problem-Solving & Structural Thinking – When presented with highly complex or ambiguous scenarios, such as designing a bidding strategy or diagnosing a product regression, you must demonstrate a logical, step-by-step approach. Break down the problem, state your assumptions clearly, and evaluate trade-offs out loud.

Execution & Coding Efficiency – You need to write clean, production-grade code quickly. This applies to both your algorithmic coding assessments and your data manipulation tasks. Familiarity with vectorization in numpy and writing highly optimized SQL queries is essential.

Collaboration & Cultural Alignment – At Moloco, we move fast and solve highly complex, distributed problems. You should be prepared to discuss your past projects with a focus on ownership, technical leadership, and how you navigated ambiguity to deliver business impact.

The interview loop for a Data Scientist at Moloco is comprehensive and highly standardized to ensure technical excellence. The process typically begins with an initial resume screen where recruiters look closely at your past experience in machine learning, statistics, and large-scale data systems. Candidates who pass this screening are invited to complete an Online Assessment (OA).

Following a successful online assessment, you will move to a technical screening phase, often referred to as a "technical handshake" or technical screen. This is a live session with a senior team member that combines deep-dive technical questions, coding, and basic statistical problem-solving. If you perform well, you will proceed to the final onsite loop, which consists of multiple back-to-back technical and behavioral interviews focusing on system design, advanced mathematics, and cultural fit.

The timeline above outlines the typical progression from your initial application to the final decision. The process is designed to be highly rigorous, particularly in the early stages, with a heavy emphasis on filtering for strong technical capabilities before moving to deep-dive team discussions. You should expect the entire process to take between three to six weeks, depending on scheduling availability and location.

Probability, Statistics, and Mathematical Modeling

This evaluation area lies at the very heart of Moloco’s business. Because our core product relies on real-time bidding and predictive modeling in auctions, you must have an exceptionally strong grasp of mathematical concepts. Interviewers want to see if you can translate real-world marketplace dynamics into formal mathematical frameworks.

Be ready to go over:

• Probability Distributions – Understanding when and how to apply binomial, Poisson, normal, and exponential distributions to modeling ad clicks, conversions, and bidding patterns.
• Expected Value and Game Theory – Calculating expected payoffs, optimizing strategies under uncertainty, and understanding auction mechanics (e.g., first-price vs. second-price auctions).
• Hypothesis Testing – Formulating null hypotheses, calculating p-values, understanding Type I and Type II errors, and determining sample sizes required for statistical power.
• Advanced concepts (less common) – Multi-armed bandits for ad exploration, Bayesian optimization, and Markov chains.

Example scenarios:

• "You are designing a bidding agent for a first-price ad auction. If you know the distribution of the highest competing bid is uniform between $0 and$10, what is your optimal bid to maximize expected profit?"
• "Explain how you would set up a multi-armed bandit framework to dynamically allocate traffic to different ad creatives while minimizing regret."

Coding, SQL, and Data Manipulation

As a Data Scientist, you will interact with massive, complex datasets daily. You must be able to query, clean, and manipulate data efficiently to extract insights or prepare features for model training.

Be ready to go over:

• Dataframe Manipulation – Performing merges, aggregations, window operations, and vectorization efficiently in Python using libraries like numpy and pandas.
• SQL Mastery – Writing complex queries involving window functions, common table expressions (CTEs), and complex joins to aggregate event-level ad logs.
• Algorithmic Problem Solving – Solving data structures and algorithms questions (typically LeetCode Medium level) focusing on arrays, hash maps, two pointers, and search algorithms.
• Advanced concepts (less common) – Optimizing memory footprint when processing large dataframes, parallel processing in Python, and understanding query execution plans in SQL.

Example scenarios:

• "Write a SQL query to find the top 3 high-performing ad campaigns by conversion rate for each country, excluding campaigns with fewer than 1,000 impressions."
• "Given an array of timestamped user actions, write an efficient Python function to find the longest session where a user did not experience more than a 5-minute gap between actions."

Machine Learning Theory & System Design

This area tests your theoretical understanding of machine learning models and your practical ability to architect scalable, real-time ML systems. At Moloco, our models must be both highly accurate and incredibly fast.

Be ready to go over:

• Model Training Mechanics – Loss functions, optimization algorithms (e.g., Adam, SGD), and the mathematical intuition behind gradient boosting and deep neural networks.
• Feature Engineering – Handling high-cardinality categorical features (like user IDs or publisher domains), target encoding, and managing missing data.
• Evaluation Metrics – Selecting and optimizing appropriate metrics (e.g., AUC-ROC, Log Loss, Precision-Recall curves) based on specific business objectives.
• Advanced concepts (less common) – Real-time feature stores, deep learning architectures for recommendation systems, and model distillation for low-latency inference.

Example scenarios:

• "How would you design a machine learning system to predict the probability of a user installing an app after clicking an ad, taking into account extreme class imbalance and cold-start apps?"
• "Walk me through the mathematical details of how Gradient Boosting Decision Trees (GBDT) minimize loss, and explain how you would configure its hyperparameters to prevent overfitting."

Product Sense & Experimentation

Our data scientists do not work in a vacuum; they must understand how their technical decisions impact Moloco’s business and our advertisers' bottom lines. This area evaluates your product intuition and experimental rigor.

Be ready to go over:

• Metric Design – Defining and validating key performance indicators (KPIs) for product health, user retention, and ads integrity.
• A/B Testing Design – Setting up robust online experiments, determining randomization units, and mitigating common pitfalls like sample ratio mismatch (SRM).
• Root Cause Analysis – Investigating sudden changes in product metrics and systematically isolating the underlying factors.
• Advanced concepts (less common) – Designing experiments in double-sided marketplaces, handling network spillover effects, and synthetic control methods.

Example scenarios:

• "An advertiser notices that their conversion rate dropped significantly after we updated our bidding model, but our overall platform revenue increased. How would you investigate this conflict and what metrics would you analyze?"
• "How would you design an experimentation framework to test a new ad recommendation algorithm when users and advertisers interact in a shared, competitive marketplace?"