Better.Com Data Scientist Interview Guide

Getting ready for a Data Scientist interview at Better.com? The Better.com Data Scientist interview process typically spans 4–6 question topics and evaluates skills in areas like analytics, SQL, machine learning, probability, and Python. Interview preparation is especially vital for this role at Better.com, as candidates are expected to demonstrate technical proficiency while translating complex data into actionable insights that drive business decisions and improve customer experience. Data Scientists at Better.com are routinely tasked with designing experiments, cleaning and organizing large datasets, building predictive models, and clearly communicating findings to both technical and non-technical stakeholders.

In preparing for the interview, you should:

• Understand the core skills necessary for Data Scientist positions at Better.com.
• Gain insights into Better.com's Data Scientist interview structure and process.
• Practice real Better.com 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 Better.com Data Scientist interview process, along with sample questions and preparation tips tailored to help you succeed.

Better.Com is a digital-first homeownership company offering streamlined services in mortgage lending, real estate, title, and homeowners insurance. Since its founding in 2016, Better has funded over $45 billion in home loans and provided more than $25 billion in insurance coverage through its subsidiaries. The company has raised over $400 million in equity capital and has received notable recognition, including ranking #1 on LinkedIn’s Top Startups list in 2020 and 2021. As a Data Scientist, you will support Better’s mission to simplify and modernize the homeownership process through data-driven insights and innovative solutions.

As a Data Scientist at Better.Com, you will leverage data-driven techniques to optimize mortgage and real estate workflows, supporting the company’s mission to simplify homeownership. You will analyze large datasets to identify trends, develop predictive models, and generate actionable insights that inform product, marketing, and operations strategies. Collaborating with engineering and business teams, you will design experiments, automate data processes, and communicate findings to stakeholders. This role is essential for driving efficiency, personalizing customer experiences, and supporting Better.Com’s commitment to digital innovation in the financial technology sector.

2.1 Stage 1: Application & Resume Review

The process begins with a detailed review of your resume and application materials by the internal recruitment team. They focus on evaluating your background in analytics, proficiency with Python and SQL, experience in machine learning, and your ability to solve complex data problems. Emphasis is placed on quantifiable impact, cross-functional project experience, and your ability to communicate technical insights to both technical and non-technical audiences. To prepare, ensure your resume clearly highlights relevant data science projects, technical skills, and business impact.

2.2 Stage 2: Recruiter Screen

Next, you’ll have a phone or video call with a recruiter, typically lasting 30–45 minutes. This step assesses your motivation for joining Better.Com, alignment with the company’s mission, and a high-level overview of your technical expertise. Expect the recruiter to probe your experience with analytics, machine learning, and stakeholder communication. Preparation should include concise stories demonstrating your problem-solving approach and your ability to communicate data-driven insights.

2.3 Stage 3: Technical/Case/Skills Round

This round involves one or more interviews focused on your technical skills, including Python, SQL, machine learning, probability, and algorithmic thinking. You may encounter live coding exercises, whiteboard challenges, or case studies involving real-world business scenarios such as mortgage data analysis, user journey analytics, or designing a risk assessment model. Often, a take-home assignment is included, requiring 4–5 hours to complete a data challenge that tests your ability to clean, analyze, and interpret complex datasets. Interviewers are typically data science team members and hiring managers. To prepare, practice articulating your analytical process, coding fluency, and approach to handling ambiguous data problems.

2.4 Stage 4: Behavioral Interview

At this stage, you’ll meet with senior data scientists, team leads, or occasionally a CTO or CXO. The focus is on your collaboration, communication, and cultural fit within the organization. Expect questions about navigating project hurdles, stakeholder management, and your strategies for making data accessible to non-technical users. Preparation should center on examples that showcase your adaptability, teamwork, and ability to present complex insights clearly.

2.5 Stage 5: Final/Onsite Round

The onsite (virtual or in-person) round is typically comprised of multiple interviews with data science team members, cross-functional partners, and senior leadership. You may be asked to solve probability puzzles, write SQL queries on a whiteboard, discuss machine learning approaches, and present your take-home project. Additional unscheduled interviews may be added if you’re being considered for multiple teams or roles. Occasionally, a final 30-minute call with a CXO or executive is required to assess cultural fit and strategic alignment. Preparation should include reviewing your previous work, practicing technical explanations, and preparing for high-level business discussions.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive an offer from the recruiter, which includes details on compensation, benefits, and start date. You may have follow-up discussions with HR or leadership to clarify team placement and negotiate terms. Preparation for this stage involves researching industry standards and reflecting on your priorities for the role.

2.7 Average Timeline

The Better.Com Data Scientist interview process generally takes between 3 and 6 weeks from initial application to final offer. The standard pace involves a week between most stages, with the take-home assignment typically allotted 3–5 days. Fast-track candidates may complete the process in as little as 2–3 weeks, especially if interviews are consolidated or additional rounds are scheduled promptly. The process may extend if unscheduled interviews are added or if executive availability delays final decisions.

Now, let’s dive into the kinds of interview questions you can expect throughout this process.

3.1 SQL & Data Analytics

Expect questions that assess your ability to manipulate, aggregate, and interpret large datasets using SQL and analytical reasoning. Focus on demonstrating a deep understanding of query optimization, data cleaning, and extracting actionable insights from raw data.

3.1.1 Write a SQL query to count transactions filtered by several criterias.
Clarify the filtering requirements, structure your query with appropriate WHERE clauses, and optimize for performance. Explain how you would validate the results and handle edge cases such as missing or duplicate records.

3.1.2 Write a SQL query to compute the median household income for each city
Discuss approaches for calculating medians in SQL, such as using window functions or percentiles. Be sure to mention handling nulls and ensuring correct grouping.

3.1.3 Write a query to get the distribution of the number of conversations created by each user by day in the year 2020.
Describe how to aggregate by user and day, filter by date range, and present the results in a clear format. Consider edge cases like users with no conversations.

3.1.4 Write a query to find the engagement rate for each ad type
Explain how to calculate rates using aggregated counts, join relevant tables, and handle missing or incomplete data. Discuss the importance of normalization and segmentation.

3.1.5 Select the 2nd highest salary in the engineering department
Use ranking functions or subqueries to identify the correct value, and clarify how you would handle ties or duplicate salaries.

3.2 Machine Learning & Modeling

These questions focus on your ability to design, implement, and evaluate machine learning models. Be ready to discuss algorithm selection, feature engineering, and validation techniques relevant to business problems.

3.2.1 Creating a machine learning model for evaluating a patient's health
Outline your approach to feature selection, model choice, and validation. Address how you would handle imbalanced classes and interpret model outputs for actionable recommendations.

3.2.2 Implement logistic regression from scratch in code
Describe the mathematical foundation, step-by-step implementation, and how you would validate the model's performance. Emphasize clarity and reproducibility.

3.2.3 Building a model to predict if a driver on Uber will accept a ride request or not
Discuss your approach to feature engineering, handling categorical variables, and evaluating model accuracy. Highlight how you would address class imbalance and real-time constraints.

3.2.4 Design a data warehouse for a new online retailer
Explain your schema design, ETL process, and how you would support scalable analytics and machine learning. Discuss trade-offs between normalization and query speed.

3.2.5 Write the function to compute the average data scientist salary given a mapped linear recency weighting on the data.
Describe how to implement recency weighting, aggregate data, and ensure robustness against outliers or missing data.

3.3 Data Cleaning & Quality Assurance

These questions assess your ability to clean, organize, and validate data integrity. Demonstrate your knowledge of handling messy datasets, missing values, and ensuring reliable analysis results.

3.3.1 Describing a real-world data cleaning and organization project
Walk through your process for profiling data, identifying issues, and applying cleaning techniques. Highlight reproducibility and communication with stakeholders.

3.3.2 How would you approach improving the quality of airline data?
Explain how you would audit the data, identify sources of error, and implement systematic quality checks. Discuss collaboration with engineering or business teams.

3.3.3 Ensuring data quality within a complex ETL setup
Detail your methods for monitoring ETL pipelines, handling schema changes, and validating outputs. Emphasize documentation and automated checks.

3.3.4 You’re tasked with analyzing data from multiple sources, such as payment transactions, user behavior, and fraud detection logs. How would you approach solving a data analytics problem involving these diverse datasets? What steps would you take to clean, combine, and extract meaningful insights that could improve the system's performance?
Describe your process for data integration, resolving schema conflicts, and ensuring consistency. Discuss how you would extract insights and communicate findings.

3.3.5 Write a function to get a sample from a Bernoulli trial.
Explain the statistical principles and how you would implement and validate the sampling method.

3.4 Communication & Stakeholder Management

These questions evaluate your ability to convey complex findings to diverse audiences and manage stakeholder expectations. Focus on tailoring your communication and ensuring actionable impact.

3.4.1 How to present complex data insights with clarity and adaptability tailored to a specific audience
Discuss your strategy for simplifying technical concepts, using visualizations, and adapting to stakeholder needs.

3.4.2 Demystifying data for non-technical users through visualization and clear communication
Explain your approach to selecting appropriate visuals, storytelling, and ensuring accessibility for all audiences.

3.4.3 Making data-driven insights actionable for those without technical expertise
Describe techniques for translating findings into practical recommendations and measuring impact.

3.4.4 Strategically resolving misaligned expectations with stakeholders for a successful project outcome
Outline your process for aligning goals, communicating trade-offs, and ensuring project success.

3.4.5 How would you answer when an Interviewer asks why you applied to their company?
Share your motivations, how your skills align with the company’s mission, and demonstrate genuine interest.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Focus on a specific example where your analysis directly influenced a business outcome. Highlight the problem, your data-driven approach, and the measurable impact.

3.5.2 Describe a challenging data project and how you handled it.
Select a project with multiple hurdles—technical, organizational, or communication-based. Explain your problem-solving process and the outcome.

3.5.3 How do you handle unclear requirements or ambiguity?
Share your approach to clarifying goals, asking targeted questions, and iterating with stakeholders to refine deliverables.

3.5.4 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 facilitated open dialogue, presented evidence, and worked toward consensus.

3.5.5 Describe a time you had to negotiate scope creep when two departments kept adding “just one more” request. How did you keep the project on track?
Explain how you quantified new requests, communicated trade-offs, and maintained project integrity.

3.5.6 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Highlight your persuasion tactics, use of evidence, and collaborative approach.

3.5.7 You’re given a dataset that’s full of duplicates, null values, and inconsistent formatting. The deadline is soon, but leadership wants insights from this data for tomorrow’s decision-making meeting. What do you do?
Walk through your triage process, prioritizing must-fix issues, and communicate the limitations of your analysis.

3.5.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Share your approach to building reusable tools or scripts, and the long-term impact on team efficiency.

3.5.9 Describe a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Discuss your method for handling missing data, communicating uncertainty, and ensuring actionable recommendations.

3.5.10 How did you communicate uncertainty to executives when your cleaned dataset covered only 60% of total transactions?
Explain your strategy for transparency, using confidence intervals or caveats, and maintaining trust in your analysis.

4.1 Company-specific tips:

Demonstrate a deep understanding of the digital mortgage and real estate landscape. Study Better.Com’s mission to simplify homeownership, and be ready to discuss how data science can accelerate digital transformation in financial services. Reference specific company milestones—such as their $45 billion in home loans funded or their #1 LinkedIn Top Startups ranking—to show you’ve done your research.

Familiarize yourself with the unique challenges of mortgage lending, real estate, and insurance. Consider how data science can optimize customer journeys, personalize offers, and reduce risk. Be prepared to discuss how you would use data to streamline processes, detect fraud, or enhance user experience in a highly regulated industry.

Understand Better.Com’s focus on collaboration across engineering, product, and business teams. Prepare examples that highlight your ability to work cross-functionally and communicate data insights to both technical and non-technical stakeholders. Show enthusiasm for being part of a mission-driven fintech company that values innovation and transparency.

4.2 Role-specific tips:

Demonstrate strong SQL skills by practicing queries that involve complex filtering, aggregation, and window functions. Be ready to explain your approach to problems like counting filtered transactions, calculating medians, and handling edge cases such as null values or duplicates. Practice articulating your thought process clearly, especially when discussing query optimization and validation.

Showcase your experience with machine learning by discussing real-world projects where you built and validated predictive models. Be prepared to explain your choices in algorithm selection, feature engineering, and performance evaluation. For questions about building models from scratch or handling imbalanced data, walk through your process step by step, emphasizing clarity and reproducibility.

Highlight your data cleaning expertise by sharing stories of tackling messy, incomplete, or inconsistent datasets. Detail your systematic approach to profiling, cleaning, and integrating data from multiple sources. Emphasize your commitment to data quality, reproducibility, and communication with stakeholders—especially under tight deadlines or ambiguous requirements.

Demonstrate your ability to communicate complex findings with clarity and impact. Practice explaining technical concepts in simple terms, tailoring your message to different audiences, and using visualizations to make your insights accessible. Prepare examples of how you’ve made data actionable for non-technical stakeholders, and how you’ve measured the business impact of your recommendations.

Prepare for behavioral questions by reflecting on past experiences where you drove decisions with data, navigated ambiguity, or influenced without authority. Use the STAR method (Situation, Task, Action, Result) to structure your answers, and focus on outcomes that align with Better.Com’s values of innovation, collaboration, and customer-centricity.

Be ready to discuss your approach to stakeholder management, especially in situations where priorities shift or expectations are misaligned. Share strategies for aligning goals, communicating trade-offs, and ensuring successful project outcomes. Show that you can balance technical rigor with pragmatic business needs.

Finally, review your past projects and be prepared to present a take-home assignment or portfolio piece. Practice telling a compelling story that covers your analytical process, technical decisions, and the real-world impact of your work. This will help you stand out as a well-rounded candidate who can drive Better.Com’s mission forward through data science.

5.1 “How hard is the Better.Com Data Scientist interview?”
The Better.Com Data Scientist interview is challenging and thorough, focusing on both technical depth and business acumen. You’ll need to demonstrate proficiency in SQL, Python, machine learning, and analytics, as well as the ability to translate complex data into actionable business insights. The process tests not only your technical skills but also your communication, problem-solving, and stakeholder management abilities. Candidates with strong data cleaning, modeling, and business communication skills will find themselves well-prepared.

5.2 “How many interview rounds does Better.Com have for Data Scientist?”
Typically, the Better.Com Data Scientist interview consists of 4 to 6 rounds. These include an initial recruiter screen, a technical/case study round (often with a take-home assignment), a behavioral interview, and a final onsite or virtual panel with cross-functional stakeholders and leadership. Additional interviews may be added for candidates being considered by multiple teams.

5.3 “Does Better.Com ask for take-home assignments for Data Scientist?”
Yes, most candidates for the Data Scientist role at Better.Com are given a take-home assignment. This assignment usually involves a real-world data challenge that tests your ability to clean, analyze, and interpret large datasets, as well as communicate your findings clearly. Expect to spend 4–5 hours on this task, and be prepared to discuss your approach during subsequent interviews.

5.4 “What skills are required for the Better.Com Data Scientist?”
Key skills include advanced SQL, Python programming, machine learning model development, statistical analysis, and data cleaning. You should also be adept at designing experiments, building predictive models, and communicating technical findings to both technical and non-technical stakeholders. Experience with business analytics, stakeholder management, and data-driven decision-making in a fintech or real estate context will give you a strong advantage.

5.5 “How long does the Better.Com Data Scientist hiring process take?”
The hiring process typically takes 3 to 6 weeks from application to offer. Timelines can vary depending on candidate availability, scheduling logistics, and the number of interview rounds. The process may move faster for fast-track candidates or extend if additional interviews are required.

5.6 “What types of questions are asked in the Better.Com Data Scientist interview?”
You will encounter a mix of technical and behavioral questions. Technical questions cover SQL queries, data analytics, machine learning, probability, and coding in Python. You’ll also be asked to solve real-world business problems, design experiments, and present your take-home assignment. Behavioral questions assess your approach to data-driven decision-making, collaboration, stakeholder management, and communication of complex insights.

5.7 “Does Better.Com give feedback after the Data Scientist interview?”
Better.Com typically provides high-level feedback through recruiters. While you may receive general input on your performance or fit, detailed technical feedback is less common, especially in later stages. If you’re not selected, you can always request feedback for future improvement.

5.8 “What is the acceptance rate for Better.Com Data Scientist applicants?”
The Data Scientist role at Better.Com is highly competitive, with an estimated acceptance rate of 3–5% for qualified applicants. The company seeks candidates who excel in both technical execution and business impact, so thorough preparation is key to standing out.

5.9 “Does Better.Com hire remote Data Scientist positions?”
Yes, Better.Com does offer remote opportunities for Data Scientists. While some roles may require occasional visits to company offices for collaboration or onboarding, many positions are fully remote or offer flexible work arrangements, reflecting the company’s commitment to digital-first operations.

• Related guides:
  • Better.Com interview questions
  • Data Scientist interview guide
  • Top data science interview tips

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

With resources like the Better.Com 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.

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!