Amobee Data Scientist Interview Guide

Getting ready for a Data Scientist interview at Amobee? The Amobee Data Scientist interview process typically spans a range of technical and problem-solving question topics, evaluating skills in probability theory, SQL, machine learning algorithms, and data-driven business analysis. As a leading digital advertising technology company, Amobee leverages advanced analytics and large-scale data infrastructure to optimize marketing performance and drive measurable results for clients. Data Scientists at Amobee are responsible for designing scalable data pipelines, developing predictive models, analyzing diverse datasets, and translating complex findings into actionable business insights that support data-driven decision-making across digital marketing campaigns.

In this role, you can expect to work on projects such as implementing machine learning solutions for campaign optimization, conducting rigorous A/B testing, building ETL pipelines, and communicating insights to both technical and non-technical stakeholders. The position requires a strong focus on analytical rigor and the ability to deliver clear, impactful recommendations in a fast-paced, results-oriented environment. This guide will help you prepare for your Amobee Data Scientist interview by outlining the core skills assessed, providing deep insight into the interview structure, and offering tailored sample questions and preparation strategies to give you a competitive edge.

Amobee is a leading digital advertising technology company specializing in data-driven solutions for cross-channel media planning, buying, and analytics. Serving global brands, agencies, and publishers, Amobee leverages advanced machine learning and big data to optimize digital campaigns across TV, digital, and social platforms. The company’s mission is to empower marketers with actionable insights and unified data to maximize advertising effectiveness. As a Data Scientist, you will play a critical role in developing predictive models and analytics that drive innovation and enhance Amobee’s ability to deliver impactful advertising outcomes.

As a Data Scientist at Amobee, you will analyze large-scale digital advertising and user engagement data to uncover insights that drive campaign performance and product innovation. You will collaborate with engineering, product, and analytics teams to develop predictive models, optimize targeting algorithms, and support data-driven decision making across the organization. Key responsibilities include designing experiments, building machine learning solutions, and visualizing complex data to communicate findings to stakeholders. This role is central to enhancing Amobee’s advertising technology platform, helping clients maximize ROI and improve audience reach through advanced analytics.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your application and resume, focusing on your experience with SQL, machine learning, algorithms, and probability. Amobee’s data science team looks for candidates who have demonstrated proficiency in designing and implementing data pipelines, building predictive models, and conducting statistical analyses. Emphasize relevant project experience, especially those involving large-scale data processing, ETL, and business impact. To prepare, ensure your resume highlights technical depth, quantifiable achievements, and clear communication around your data science contributions.

2.2 Stage 2: Recruiter Screen

This initial conversation is typically a brief call with a recruiter to confirm your interest in Amobee and the data scientist role, discuss your background, and outline the interview process. Expect questions about your motivation for joining the company, your core technical competencies, and your experience working with diverse data sets. Preparation should involve reviewing your resume, articulating your career goals, and expressing enthusiasm for Amobee’s data-driven culture.

2.3 Stage 3: Technical/Case/Skills Round

The technical interview is a core part of Amobee’s process and usually involves multiple interviewers over a 90-minute session. You’ll be assessed on SQL querying, probability theory, machine learning algorithms, and data problem-solving. Expect to tackle real-world scenarios such as designing ETL pipelines, analyzing user journeys, measuring experiment success rates, and building predictive models. Interviewers will evaluate your analytical thinking, coding ability, and approach to challenges like data cleaning, handling imbalanced datasets, and extracting insights from heterogeneous sources. Preparation should include practicing SQL, revisiting probability and statistics fundamentals, and reviewing machine learning concepts relevant to digital advertising and user analytics.

2.4 Stage 4: Behavioral Interview

Amobee values candidates who can communicate complex data insights with clarity and adapt their message for different audiences. This stage may be integrated into the technical round or conducted separately, focusing on your collaboration skills, presentation style, and ability to demystify technical content for stakeholders. Expect to discuss your approach to cross-functional teamwork, handling project hurdles, and making data actionable for non-technical users. Prepare by reflecting on past experiences where you translated analytics into business strategy and overcame obstacles in data projects.

2.5 Stage 5: Final/Onsite Round

The final stage typically involves meeting with senior team members or the hiring manager. This session may include a mix of technical deep-dives, case studies, and behavioral questions, with an emphasis on your strategic thinking and fit with Amobee’s culture. You may be asked to walk through a data science project end-to-end, explain your decision-making process, and demonstrate your ability to design scalable solutions for marketing analytics or ad tech environments. Preparation should focus on structuring your responses, showcasing leadership in data projects, and articulating your impact in previous roles.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete the interviews, Amobee’s recruiter will reach out to discuss the offer, compensation package, and onboarding timeline. This stage involves clarifying any questions about the role, negotiating terms, and confirming your start date. Prepare by researching market compensation trends, knowing your priorities, and being ready to discuss your expectations confidently.

2.7 Average Timeline

The typical Amobee Data Scientist interview process spans 2-4 weeks from initial application to final offer. Fast-track candidates with highly relevant experience or internal referrals may move through the process more quickly, sometimes within 1-2 weeks. Standard pacing allows for scheduling flexibility and thorough evaluation, with most stages occurring within a week of each other. Technical rounds are usually completed in a single session, and behavioral or final interviews may be scheduled separately based on team availability.

Next, let’s dive into the specific interview questions you can expect during each stage of the Amobee Data Scientist process.

3.1 SQL & Data Manipulation

Expect questions that test your ability to efficiently query, aggregate, and manipulate large datasets. Be prepared to demonstrate your understanding of SQL logic, data cleaning, and handling real-world data challenges.

3.1.1 Write a SQL query to count transactions filtered by several criterias.
Focus on constructing queries with multiple WHERE conditions, grouping, and filtering. Be clear about handling edge cases and optimizing for performance.

3.1.2 Write a query to calculate the conversion rate for each trial experiment variant
Aggregate data by experiment group, calculate conversion rates, and discuss how you handle missing or incomplete data.

3.1.3 Write a query to compute the average time it takes for each user to respond to the previous system message
Use window functions or self-joins to align events, compute time differences, and aggregate results by user.

3.1.4 Modifying a billion rows in a large database
Discuss strategies for updating massive tables, including batching, indexing, and minimizing downtime or locking issues.

3.1.5 Describing a real-world data cleaning and organization project
Explain your approach to profiling, cleaning, and structuring messy data, including tools and methodologies you use.

3.2 Machine Learning & Modeling

These questions assess your experience with designing, deploying, and evaluating machine learning models. Emphasize your approach to feature engineering, model selection, and dealing with real-world data complexities.

3.2.1 Addressing imbalanced data in machine learning through carefully prepared techniques.
Describe strategies such as resampling, synthetic data generation, or algorithmic adjustments to handle class imbalance.

3.2.2 Identify requirements for a machine learning model that predicts subway transit
Outline how you would scope the problem, gather relevant features, select model types, and evaluate performance.

3.2.3 Building a model to predict if a driver on Uber will accept a ride request or not
Discuss feature engineering, handling class imbalance, and evaluation metrics specific to binary classification.

3.2.4 Generating Discover Weekly - building a recommendation engine
Explain collaborative filtering, content-based methods, or hybrid approaches for personalized recommendations.

3.2.5 Designing an ML system to extract financial insights from market data for improved bank decision-making
Describe building end-to-end pipelines, integrating APIs, and ensuring model robustness in production settings.

3.3 Experimentation & Statistics

You’ll be expected to demonstrate your ability to design experiments, interpret statistical results, and communicate findings. Focus on A/B testing, hypothesis testing, and statistical rigor.

3.3.1 The role of A/B testing in measuring the success rate of an analytics experiment
Explain how you design experiments, choose metrics, ensure statistical significance, and interpret results.

3.3.2 How to evaluate whether a 50% rider discount promotion is a good or bad idea, and what metrics to track
Discuss experiment design, relevant KPIs (e.g., retention, revenue), and how to analyze causal impact.

3.3.3 How would you measure the success of an email campaign?
Identify key metrics (open rates, conversions), control groups, and considerations for statistical validity.

3.3.4 How would you analyze how the feature is performing?
Outline how you would define success, set up tracking, and use statistical analysis to interpret results.

3.3.5 Non-normal A/B testing and how to handle it
Describe non-parametric alternatives, bootstrapping, or data transformations when standard assumptions are violated.

3.4 Data Engineering & Pipelines

These questions assess your ability to design robust, scalable data pipelines and manage complex data flows. Highlight your understanding of ETL, real-time streaming, and data warehousing best practices.

3.4.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Discuss modular pipeline design, data validation, and strategies for handling schema changes.

3.4.2 Redesign batch ingestion to real-time streaming for financial transactions.
Explain the trade-offs between batch and streaming, tools you’d use, and ensuring data consistency.

3.4.3 Design a data pipeline for hourly user analytics.
Describe data ingestion, transformation, aggregation, and how you’d ensure performance at scale.

3.4.4 How would you approach improving the quality of airline data?
Share your framework for data validation, anomaly detection, and building automated quality checks.

3.4.5 Design a data warehouse for a new online retailer
Outline schema design, data partitioning, and strategies for supporting analytics and reporting needs.

3.5 Communication & Data Storytelling

Strong communication skills are critical. You’ll be expected to translate complex analyses into actionable insights for non-technical audiences and tailor your message to different stakeholders.

3.5.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Describe how you structure presentations, adapt language, and use visuals to maximize impact.

3.5.2 Making data-driven insights actionable for those without technical expertise
Share techniques for simplifying technical concepts and focusing on business value.

3.5.3 Demystifying data for non-technical users through visualization and clear communication
Explain your approach to choosing the right visualization and ensuring accessibility.

3.5.4 How would you answer when an Interviewer asks why you applied to their company?
Articulate your motivation, alignment with company values, and specific interest in the role.

3.5.5 What kind of analysis would you conduct to recommend changes to the UI?
Discuss user journey mapping, identifying pain points, and translating quantitative findings into actionable UI recommendations.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision. What was the outcome and how did you communicate your recommendation to stakeholders?

3.6.2 Describe a challenging data project and how you handled it. What obstacles did you face and how did you overcome them?

3.6.3 How do you handle unclear requirements or ambiguity in a data science project?

3.6.4 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.

3.6.5 Walk us through how you handled conflicting KPI definitions between two teams and arrived at a single source of truth.

3.6.6 Describe a time you had to negotiate scope creep when multiple departments kept adding requests to a project. How did you keep the project on track?

3.6.7 Tell us about a time you delivered critical insights despite a significant portion of the dataset having null values. What analytical trade-offs did you make?

3.6.8 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.

3.6.9 Give an example of automating recurrent data-quality checks so a dirty-data crisis didn’t happen again.

3.6.10 Describe a project where you owned end-to-end analytics—from raw data ingestion to final visualization. What were your key learnings?

4.1 Company-specific tips:

Immerse yourself in Amobee’s core business: digital advertising technology and cross-channel media analytics. Understand how Amobee leverages large-scale data, advanced machine learning, and unified analytics to optimize marketing campaigns for global brands and agencies. Review Amobee’s major products, their approach to media buying, and how data science drives campaign performance and ROI. Be prepared to discuss how data-driven solutions can solve real-world advertising challenges such as audience targeting, attribution, and budget allocation.

Stay current on trends in ad tech, including privacy regulations, cookie-less tracking, and the evolution of cross-platform measurement. Familiarize yourself with common metrics in digital advertising, such as click-through rates, conversion rates, and attribution models. Demonstrate your awareness of the competitive landscape and how Amobee differentiates itself through data innovation and actionable insights.

Reflect on Amobee’s collaborative culture and its emphasis on translating complex analytics into clear recommendations for clients and stakeholders. Prepare examples of how you have communicated technical findings to non-technical audiences, and think about how you would contribute to a fast-paced, results-oriented team focused on maximizing advertising effectiveness.

4.2 Role-specific tips:

4.2.1 Master SQL for large-scale data manipulation and campaign analytics.
Practice writing complex SQL queries that aggregate, filter, and join massive datasets typical in digital advertising. Be ready to discuss strategies for efficiently updating billions of rows, optimizing queries for performance, and handling data cleaning tasks. Highlight your experience with window functions, time-based aggregations, and real-world scenarios such as calculating conversion rates by campaign variant or analyzing user response times.

4.2.2 Sharpen your machine learning skills with a focus on campaign optimization and user modeling.
Review algorithms commonly used in ad tech, such as logistic regression, decision trees, and recommendation systems. Prepare to discuss how you handle imbalanced datasets, engineer features for predictive modeling, and select evaluation metrics suitable for marketing applications. Be ready to walk through the end-to-end process of building a model—scoping requirements, preparing data, choosing algorithms, and interpreting results for business impact.

4.2.3 Demonstrate expertise in experimentation and statistical analysis for marketing effectiveness.
Be prepared to design and analyze A/B tests, measure the impact of promotions, and evaluate campaign success using statistical rigor. Review hypothesis testing, significance, and non-parametric alternatives for non-normal data distributions. Practice explaining how you would set up experiments, select KPIs, and interpret results to guide campaign strategy.

4.2.4 Articulate your approach to building scalable ETL pipelines and managing heterogeneous data sources.
Highlight your experience designing robust data pipelines that ingest, validate, and transform diverse datasets from multiple partners. Discuss how you would transition from batch to real-time streaming analytics, ensure data quality, and architect solutions that support high-volume hourly user analytics. Emphasize your ability to troubleshoot data issues and implement automated quality checks.

4.2.5 Showcase your communication and data storytelling abilities.
Prepare to present complex analyses in a clear, compelling manner tailored to different stakeholders. Practice structuring presentations, choosing the right visualizations, and simplifying technical concepts for business audiences. Be ready to share examples of making data actionable for non-technical users and translating user journey analytics into concrete UI recommendations.

4.2.6 Prepare for behavioral questions that assess your impact, adaptability, and collaboration.
Reflect on past projects where you used data to drive decisions, overcame ambiguity, and influenced stakeholders without formal authority. Think about how you handled conflicting KPIs, negotiated scope creep, and delivered insights despite challenging data quality. Be ready to discuss your experience owning analytics projects end-to-end, automating data-quality checks, and aligning diverse teams with prototypes or wireframes.

4.2.7 Connect your experience to Amobee’s mission and values.
Be authentic about your motivation for joining Amobee, emphasizing your passion for data-driven marketing and innovation. Articulate how your background aligns with the company’s focus on actionable insights, measurable results, and collaborative problem-solving. Show that you’re ready to make a meaningful impact by combining technical excellence with strategic thinking in the ad tech space.

5.1 “How hard is the Amobee Data Scientist interview?”
The Amobee Data Scientist interview is considered challenging, especially for those new to the digital advertising industry. Expect rigorous technical questions covering SQL, probability theory, machine learning, and data engineering, as well as real-world case studies that test your ability to analyze large-scale ad tech data. The process also evaluates your communication skills and your ability to translate complex analytics into actionable business insights. Candidates with strong experience in digital marketing analytics, scalable data pipelines, and statistical experimentation will find themselves well-prepared.

5.2 “How many interview rounds does Amobee have for Data Scientist?”
Typically, the Amobee Data Scientist interview process consists of five to six rounds: an application and resume review, a recruiter screen, a technical/case round, a behavioral interview, a final onsite (or virtual) round with senior stakeholders, and finally, an offer and negotiation stage. Some stages may be combined depending on team availability and candidate background.

5.3 “Does Amobee ask for take-home assignments for Data Scientist?”
While not every candidate will receive a take-home assignment, Amobee sometimes includes a technical case study or data challenge. This may involve analyzing a dataset, designing an experiment, or building a simple model to showcase your analytical approach and coding proficiency. The goal is to assess your practical skills and ability to communicate your findings clearly.

5.4 “What skills are required for the Amobee Data Scientist?”
Key skills include advanced SQL for large-scale data manipulation, strong foundations in probability and statistics, hands-on experience with machine learning algorithms, and the ability to design and optimize ETL pipelines. Familiarity with A/B testing, campaign analytics, and digital advertising metrics is highly valued. Excellent communication and data storytelling abilities are essential, as you’ll be expected to present insights to both technical and non-technical stakeholders.

5.5 “How long does the Amobee Data Scientist hiring process take?”
The typical Amobee Data Scientist interview process takes 2-4 weeks from initial application to final offer. The timeline can be shorter for candidates with highly relevant experience or internal referrals, and may extend slightly depending on scheduling constraints or the need for additional interview rounds.

5.6 “What types of questions are asked in the Amobee Data Scientist interview?”
You’ll encounter a mix of technical and behavioral questions. Technical questions span SQL querying, probability, machine learning modeling, experiment design, and data pipeline engineering. Expect real-world case studies relevant to digital advertising, such as campaign optimization or audience segmentation. Behavioral questions focus on your collaboration style, communication skills, and ability to make data-driven decisions in ambiguous situations.

5.7 “Does Amobee give feedback after the Data Scientist interview?”
Amobee generally provides feedback through your recruiter, especially if you’ve reached the later stages of the process. While detailed technical feedback may be limited, you can expect high-level insights into your performance and areas for improvement.

5.8 “What is the acceptance rate for Amobee Data Scientist applicants?”
Amobee Data Scientist roles are competitive, with an estimated acceptance rate of 3-6% for qualified applicants. Candidates who demonstrate strong technical skills, a deep understanding of marketing analytics, and the ability to communicate insights effectively have the best chance of receiving an offer.

5.9 “Does Amobee hire remote Data Scientist positions?”
Yes, Amobee does offer remote opportunities for Data Scientist roles, depending on team needs and project requirements. Some positions may require occasional office visits for collaboration or team meetings, but many teams support flexible and remote work arrangements.

• Related guides:
  • Amobee interview questions
  • Data Scientist interview guide
  • Top data science interview tips
  • Six Steps to Ace the Data Science Take Home Challenge
  • How to Land Early Career Data Science Jobs in 2025
  • Top 27 Data Science Coding Interview Questions (Updated for 2025)
  • Top 25+ Data Science SQL Interview Questions
  • Top 60 Statistics & A/B Testing Interview Questions (Updated for 2025)
  • Top 9 Machine Learning Algorithm Interview Questions for 2025
  • Top 50 Machine Learning System Design Interview Questions (2025 Guide)
  • Python Machine Learning Interview Questions Guide 2025 — Coding & Concepts
  • Top Python Interview Questions for Data Analysts & Data Scientists (2025)
  • The Data Science Career Path and Skills Progression (2025 Update)
  • Data Scientist vs Data Engineer: Which Career Path is Right for You? (Updated in 2025)

Ready to ace your Amobee Data Scientist interview? It’s not just about knowing the technical skills—you need to think like an Amobee Data Scientist, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at Amobee and similar companies.

With resources like the Amobee Data Scientist Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition. Dive deep into topics like SQL for large-scale data, machine learning for campaign optimization, A/B testing, scalable ETL pipelines, and data storytelling—each mapped to the unique challenges and expectations of Amobee’s data science team.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!