Move Data Scientist Interview Guide

Getting ready for a Data Scientist interview at Move? The Move Data Scientist interview process typically spans 4–6 question topics and evaluates skills in areas like data cleaning and organization, designing robust pipelines, statistical analysis, communicating insights to diverse audiences, and solving real-world business problems through data-driven experimentation. Interview prep is especially important for this role at Move, as candidates are expected to not only demonstrate technical proficiency with large-scale data and machine learning, but also present actionable recommendations that directly support Move’s mission of leveraging data to optimize user experiences and drive business growth.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Scientist positions at Move.
• Gain insights into Move’s Data Scientist interview structure and process.
• Practice real Move Data Scientist interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the Move Data Scientist interview process, along with sample questions and preparation tips tailored to help you succeed.

Move is a technology-driven company specializing in real estate solutions, offering digital platforms and services that connect home buyers, sellers, and real estate professionals. As a subsidiary of News Corp, Move operates well-known brands such as Realtor.com, providing comprehensive property listings, market insights, and tools to facilitate seamless real estate transactions. The company leverages data and technology to enhance the home search experience and empower informed decision-making. As a Data Scientist at Move, you will contribute to developing data-driven products and insights that drive user engagement and support the company’s mission to simplify the real estate journey.

As a Data Scientist at Move, you will leverage advanced analytical techniques and machine learning models to extract meaningful insights from large and complex datasets related to real estate and digital marketplaces. You will collaborate with cross-functional teams such as engineering, product, and marketing to develop data-driven solutions that enhance user experiences, optimize business processes, and support strategic decision-making. Typical responsibilities include building predictive models, conducting A/B testing, and presenting actionable recommendations to stakeholders. This role is integral to Move’s mission of empowering consumers and real estate professionals with reliable information and innovative tools.

2.1 Stage 1: Application & Resume Review

The initial stage involves a thorough screening of your application materials, with a focus on demonstrated experience in data science, statistical analysis, machine learning, and data pipeline development. Experience with large-scale data cleaning, data visualization, and communication of technical findings to non-technical stakeholders is highly valued. Candidates with a proven ability to drive actionable business insights from complex datasets and who highlight relevant technical skills (such as Python, SQL, and data warehousing) are most likely to advance.

2.2 Stage 2: Recruiter Screen

This round is typically a 30-minute phone call with a recruiter or talent acquisition specialist. The conversation centers on your professional background, key achievements in data-driven projects, and your motivation for joining Move. Expect to discuss your experience with cross-functional teams, your ability to translate business problems into analytical solutions, and how your career goals align with the company’s mission. Preparation should involve clear articulation of your data science journey, as well as familiarity with Move’s products and values.

2.3 Stage 3: Technical/Case/Skills Round

This stage often consists of one or two interviews, either virtual or in-person, conducted by senior data scientists or analytics managers. You may be presented with technical case studies, coding exercises, or system design questions that reflect real-world data challenges at Move. Typical topics include designing and optimizing data pipelines, handling messy or missing data, statistical modeling, evaluating the impact of business experiments (such as promotions), and communicating insights from large datasets. You should be ready to demonstrate hands-on proficiency with Python and SQL, explain your approach to data cleaning, discuss your experience with machine learning models, and walk through your methodology for extracting actionable insights from multiple data sources.

2.4 Stage 4: Behavioral Interview

In this round, interviewers assess your collaboration skills, adaptability, and communication style. You’ll be asked about your experience working with stakeholders from different backgrounds, how you handle misaligned expectations, and your strategies for making complex data accessible to non-technical audiences. Emphasis is placed on your ability to present data-driven recommendations clearly and to manage challenges in cross-functional or ambiguous environments. Prepare by reflecting on past experiences where you influenced product or business decisions, navigated project hurdles, or drove consensus among diverse teams.

2.5 Stage 5: Final/Onsite Round

The final stage typically includes a series of interviews with senior leaders, potential team members, and cross-functional partners. This may involve a technical deep-dive, a presentation of a prior data science project, or a whiteboard session to solve a business problem using data. You may be asked to discuss the design and implementation of a data warehouse, propose metrics for product evaluation, or outline a strategy for improving data quality. The goal is to evaluate both your technical depth and your ability to communicate and collaborate across business functions. Preparation should include a portfolio of impactful projects, ready-to-share examples of your analytical thinking, and a strong understanding of Move’s industry context.

2.6 Stage 6: Offer & Negotiation

Once you successfully complete all interview rounds, the recruiter will reach out to discuss the offer package, including compensation, benefits, and start date. There may be room for negotiation based on your experience and the value you bring to the team. Be prepared to articulate your expectations and clarify any outstanding questions about the role or company culture.

2.7 Average Timeline

The typical Move Data Scientist interview process spans 3–5 weeks from application to offer, with each stage generally taking about one week. Fast-track candidates with highly relevant experience or internal referrals may complete the process in as little as 2–3 weeks, while scheduling for later-stage interviews can extend the timeline depending on team availability. Take-home assignments, if included, usually have a 3–5 day deadline for completion, and the onsite round is often scheduled within a week of successful technical and behavioral interviews.

Next, let’s dive into the specific types of interview questions you can expect throughout the Move Data Scientist process.

Below are common interview questions for Data Scientist roles at Move. The technical interview at Move will probe your problem-solving skills with real-world data challenges, your ability to design scalable data solutions, and your communication of complex insights to both technical and non-technical stakeholders. Be ready to discuss both your technical knowledge and your approach to collaboration, ambiguity, and business impact.

3.1 Data Analysis & Experimentation

Expect questions focusing on your ability to design experiments, analyze business scenarios, and extract actionable insights from data. These questions assess not only your analytical rigor but also your strategic thinking and creativity in solving open-ended problems.

3.1.1 You work as a data scientist for a ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe how you would set up an experiment (A/B test or quasi-experiment), select relevant KPIs (e.g., revenue, retention, LTV, cannibalization), and measure the promotion’s impact while accounting for confounders.

3.1.2 We're interested in determining if a data scientist who switches jobs more often ends up getting promoted to a manager role faster than a data scientist that stays at one job for longer.
Explain how you’d design a study using available career data, choose the right statistical tests, and control for confounding factors such as company size or industry.

3.1.3 How would you estimate the number of gas stations in the US without direct data?
Demonstrate your approach to estimation problems using logical breakdowns, available proxies, and making reasonable assumptions.

3.1.4 Let's say you work at Facebook and you're analyzing churn on the platform.
Discuss how you would define and measure churn, segment users, and identify drivers of retention rate disparities across cohorts.

3.2 Data Engineering & Pipeline Design

These questions evaluate your skill in designing robust, scalable data pipelines and systems that support analytics and machine learning. You’ll be expected to discuss architectural choices, data quality, and efficiency.

3.2.1 Design a data pipeline for hourly user analytics.
Outline the end-to-end pipeline, including data ingestion, transformation, aggregation, storage, and monitoring.

3.2.2 Migrating a social network's data from a document database to a relational database for better data metrics
Describe your migration strategy, potential challenges, and how you’d ensure data integrity and minimal downtime.

3.2.3 How would you systematically diagnose and resolve repeated failures in a nightly data transformation pipeline?
Provide a structured approach to root cause analysis, logging, monitoring, and implementing long-term fixes.

3.2.4 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Discuss data sources, ETL processes, model integration, and serving predictions in a scalable way.

3.3 Machine Learning & Modeling

Move expects candidates to have hands-on experience building, validating, and interpreting machine learning models. Questions in this area probe your understanding of algorithms, model evaluation, and real-world deployment.

3.3.1 Identify requirements for a machine learning model that predicts subway transit
List the data features, modeling approach, evaluation metrics, and deployment considerations for such a predictive system.

3.3.2 Why would one algorithm generate different success rates with the same dataset?
Discuss factors like random initialization, data splits, hyperparameter tuning, and data preprocessing that can affect model performance.

3.3.3 How would you analyze how the feature is performing?
Describe your approach to feature performance analysis, including defining success metrics, segmenting users, and conducting statistical tests.

3.3.4 How would you approach solving a data analytics problem involving multiple sources, such as payment transactions, user behavior, and fraud detection logs?
Explain how you’d clean, join, and analyze disparate datasets to extract insights and improve system performance.

3.4 Data Cleaning, Quality & Communication

You’ll be expected to discuss your experience with messy or incomplete data, as well as your ability to communicate findings to both technical and non-technical audiences.

3.4.1 Describing a real-world data cleaning and organization project
Walk through your process for profiling, cleaning, and validating data, and the impact your work had on downstream analytics.

3.4.2 How to present complex data insights with clarity and adaptability tailored to a specific audience
Explain your approach to tailoring data stories, visualizations, and recommendations to different stakeholders.

3.4.3 Demystifying data for non-technical users through visualization and clear communication
Share techniques for making data accessible and actionable to a broad audience, focusing on simplicity and relevance.

3.4.4 Making data-driven insights actionable for those without technical expertise
Illustrate how you translate technical findings into clear, business-focused recommendations.

3.5 Statistics & Data Interpretation

These questions assess your command of statistical concepts and your ability to explain them in practical, intuitive terms.

3.5.1 Explain a p-value to a non-technical stakeholder
Use analogies and simple language to convey what a p-value represents and why it matters in decision-making.

3.5.2 Why would one algorithm generate different success rates with the same dataset?
Discuss sources of statistical variance, including data splits, randomness, and sample size effects.

3.5.3 Describe challenges of specific student test score layouts, recommended formatting changes for enhanced analysis, and common issues found in "messy" datasets.
Detail your process for restructuring, cleaning, and validating educational data for robust analysis.

3.6 Behavioral Questions

3.6.1 Tell me about a time you used data to make a decision.
Focus on a scenario where your analysis directly influenced a business or product outcome. Highlight how you translated insights into recommendations and the impact it had.

3.6.2 Describe a challenging data project and how you handled it.
Choose a project with significant obstacles (technical, organizational, or data quality). Explain your approach to overcoming these challenges and the results you achieved.

3.6.3 How do you handle unclear requirements or ambiguity?
Share a story where requirements were shifting or vague. Emphasize how you clarified objectives, iterated quickly, and kept stakeholders aligned.

3.6.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Describe how you fostered collaboration, listened actively, and used data to build consensus.

3.6.5 Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
Discuss how you adapted your communication style, used visualizations or analogies, and ensured your message was understood.

3.6.6 Give an example of how you balanced short-term wins with long-term data integrity when pressured to ship a dashboard quickly.
Explain the trade-offs you made, how you communicated risks, and the steps you took to ensure future improvements.

3.6.7 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Highlight your persuasive skills, use of evidence, and ability to build trust across teams.

3.6.8 Walk us through how you handled conflicting KPI definitions (e.g., “active user”) between two teams and arrived at a single source of truth.
Explain your process for gathering requirements, facilitating discussions, and aligning on standardized metrics.

3.6.9 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss how you assessed data quality, communicated uncertainty, and ensured your findings were actionable.

3.6.10 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Share the tools or scripts you implemented, the process improvements made, and the impact on team efficiency.

4.1 Company-specific tips:

Immerse yourself in Move's mission to simplify the real estate journey through data and technology. Understand how Move leverages data to optimize user experiences on platforms like Realtor.com, and familiarize yourself with the types of data Move collects—property listings, user interactions, market trends, and transaction histories.

Research recent innovations and product launches at Move, such as new features on Realtor.com or partnerships that impact how users search for homes. Be prepared to discuss how data science can drive engagement and create value for both consumers and real estate professionals.

Explore the competitive landscape of digital real estate platforms. Know what differentiates Move from other players, and be ready to articulate how data-driven insights can strengthen Move’s market position and enhance its offerings.

4.2 Role-specific tips:

4.2.1 Demonstrate expertise in cleaning and organizing large, messy real estate datasets.
Showcase your experience handling complex datasets with issues like missing values, inconsistent formatting, and duplicate records. Be ready to walk through your data profiling, cleaning, and validation process, emphasizing how your work led to more reliable analytics and better business decisions.

4.2.2 Practice designing robust and scalable data pipelines for real-time and batch analytics.
Prepare to outline end-to-end solutions for ingesting, transforming, and aggregating data—especially for user analytics or predictive modeling. Discuss your architectural choices, how you ensure data quality, and methods for monitoring pipeline health and resolving failures.

4.2.3 Highlight your ability to design and analyze experiments that drive business outcomes.
Expect questions on setting up A/B tests or quasi-experiments to measure the impact of product changes or promotions. Explain how you select key metrics, control for confounders, and interpret results to recommend actionable strategies for Move’s platforms.

4.2.4 Showcase your hands-on machine learning experience, especially with modeling user behavior and market trends.
Be prepared to discuss the full lifecycle of a predictive modeling project—from feature engineering and algorithm selection to model validation and deployment. Relate your experience to real estate scenarios, such as predicting homebuyer intent or optimizing listing recommendations.

4.2.5 Communicate complex insights clearly to both technical and non-technical stakeholders.
Practice tailoring your explanations and visualizations for diverse audiences, focusing on clarity and relevance. Share examples of how you’ve made data accessible and actionable for product managers, marketers, or executives.

4.2.6 Articulate your approach to extracting insights from multiple, disparate data sources.
Demonstrate your ability to join and analyze data from sources like payment transactions, user activity logs, and external market data. Discuss how you resolve data integration challenges and translate findings into strategic recommendations.

4.2.7 Be ready to explain statistical concepts and data-driven decisions in intuitive terms.
Prepare analogies and simple explanations for concepts like p-values, statistical significance, and model variance. Show your ability to make these ideas understandable and relevant to business decision-makers.

4.2.8 Reflect on your experience influencing stakeholders and driving consensus through data.
Share stories where you navigated ambiguous requirements, conflicting KPI definitions, or resistance to change. Emphasize your collaborative approach, adaptability, and ability to build trust across teams.

4.2.9 Prepare examples of balancing speed and data integrity under tight deadlines.
Discuss situations where you shipped dashboards or analytics quickly, the trade-offs you made, and how you ensured long-term data quality. Highlight your commitment to continuous improvement and automation of data-quality checks.

4.2.10 Review your portfolio for impactful, business-driven data science projects.
Select projects that demonstrate your technical depth, strategic thinking, and ability to drive measurable outcomes. Be ready to present your methodology, results, and the value your work delivered to stakeholders.

5.1 How hard is the Move Data Scientist interview?
The Move Data Scientist interview is considered moderately challenging, with a strong emphasis on practical data science skills, business acumen, and the ability to communicate complex insights clearly. Candidates are expected to demonstrate proficiency in data cleaning, pipeline design, statistical analysis, and machine learning, as well as a strategic mindset for solving real-world problems in the digital real estate domain. The interview process rewards those who can connect technical solutions to business impact, making preparation and domain knowledge key to success.

5.2 How many interview rounds does Move have for Data Scientist?
Move typically conducts 4–6 interview rounds for Data Scientist roles. The process includes an initial application and resume review, a recruiter screen, one or more technical/case/skills interviews, a behavioral interview, a final onsite or virtual round with senior leaders, and an offer/negotiation stage. Each round is designed to assess a different aspect of your skills and fit for the team.

5.3 Does Move ask for take-home assignments for Data Scientist?
Yes, Move may include a take-home assignment as part of the technical or case interview stage. These assignments often involve analyzing a dataset, designing an experiment, or building a simple predictive model. Expect to spend 3–5 days on the assignment, demonstrating your ability to solve practical business problems using data science techniques relevant to Move’s platforms and users.

5.4 What skills are required for the Move Data Scientist?
Key skills for Move Data Scientists include advanced proficiency in Python and SQL, expertise in data cleaning and organization, experience designing scalable data pipelines, statistical analysis, machine learning modeling, and the ability to communicate insights to both technical and non-technical stakeholders. Familiarity with real estate data, A/B testing, and business experimentation is highly valued, as is a collaborative approach to working with cross-functional teams.

5.5 How long does the Move Data Scientist hiring process take?
The typical Move Data Scientist hiring process takes 3–5 weeks from application to offer. Each interview stage generally lasts about one week, though fast-track candidates or those with internal referrals may complete the process in as little as 2–3 weeks. Scheduling for later-stage interviews may vary depending on team availability and candidate schedules.

5.6 What types of questions are asked in the Move Data Scientist interview?
Expect a mix of technical and behavioral questions. Technical questions cover data cleaning, pipeline design, statistical modeling, machine learning, and real-world business cases (e.g., designing experiments or optimizing product metrics). Behavioral questions focus on collaboration, communication, handling ambiguity, and influencing stakeholders. You may also be asked to present previous projects or walk through your approach to solving open-ended data challenges.

5.7 Does Move give feedback after the Data Scientist interview?
Move typically provides high-level feedback through recruiters, especially regarding your fit and performance in technical and behavioral rounds. While detailed technical feedback may be limited, you can expect to hear whether your skills and experience align with the team’s needs and any next steps in the process.

5.8 What is the acceptance rate for Move Data Scientist applicants?
While Move does not publicly disclose specific acceptance rates, the Data Scientist role is competitive given the company’s reputation and the technical demands of the position. Industry estimates suggest an acceptance rate of approximately 3–7% for qualified applicants who advance past the initial screening stages.

5.9 Does Move hire remote Data Scientist positions?
Yes, Move offers remote opportunities for Data Scientist roles, with some positions allowing for fully remote work and others requiring occasional visits to the office for team collaboration or key meetings. Flexibility depends on the specific team and business needs, so clarify expectations during the interview process.

• Related guides:
  • Move interview questions
  • Data Scientist interview guide
  • Top Data Science interview tips

Ready to Ace Your Interview?

Ready to ace your Move Data Scientist interview? It’s not just about knowing the technical skills—you need to think like a Move 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 Move and similar companies.

With resources like the Move 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.

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