1. What is a Research Scientist at Betterup?

As a Research Scientist at Betterup, you are at the forefront of combining behavioral science, data analytics, and cutting-edge technology to drive human transformation. Your work directly informs how Betterup measures coaching efficacy, understands user well-being, and develops innovative product features. You are not just analyzing data; you are uncovering the psychological and behavioral insights that empower individuals and organizations to thrive.

The impact of this position is massive. You will sit at the intersection of the Betterup Labs division, product teams, and engineering, translating complex human behavior into measurable, actionable science. Whether you are designing rigorous behavioral studies, analyzing massive datasets of coaching interactions, or exploring how emerging technologies like large language models (LLMs) and GPT can be applied to coaching frameworks, your research shapes the future of the platform.

Expect a dynamic, fast-paced startup environment. The role demands a unique blend of scientific rigor and entrepreneurial agility. You will be expected to advocate for robust study designs while remaining flexible enough to pivot toward highly visible, strategic business priorities. If you are passionate about human potential and thrive in a mission-driven culture, this role offers an unparalleled opportunity to scale your research to millions of users.

2. Common Interview Questions

The questions below represent the types of challenges you will face during the Betterup interview process. They are designed to test both your technical depth and your ability to apply scientific thinking to real-world product scenarios.

Technical & Statistical Questions

These questions assess your foundational knowledge of statistics and your ability to manipulate data.

• Walk me through how you would conduct a power analysis for a new behavioral intervention.
• How do you handle outliers in a dataset measuring user engagement?
• Write a function in R to calculate the rolling average of user assessment scores over a 30-day window.
• Explain the difference between fixed and random effects in a mixed-effects model.
• How would you test if a newly introduced product feature caused a statistically significant increase in user retention?

Study Design & Applied Research

These questions test your ability to structure scientific inquiries around Betterup's specific product offerings.

• Design a study to validate the effectiveness of a new coaching methodology for enterprise managers.
• If we want to use an LLM to summarize coaching sessions, how would you design a research plan to ensure the summaries are accurate and unbiased?
• How would you measure the long-term impact of a 6-week mental fitness program?
• What are the risks of using self-reported survey data to measure behavioral change, and how do you mitigate them?
• Design an A/B test for a new onboarding flow. What are your primary and secondary metrics?

Behavioral & Mission Alignment

These questions evaluate your cultural fit and your passion for the work.

• Why are you passionate about the science of coaching and human transformation?
• Tell me about a time your research findings contradicted the assumptions of a product team. How did you handle it?
• Where do you see your research interests taking you in the next few years?
• Describe a time you had to explain a complex statistical concept to an executive with no technical background.
• How do you prioritize your research initiatives when working in a fast-paced, ambiguous startup environment?

3. Getting Ready for Your Interviews

Preparing for the Betterup interview requires balancing your deep technical expertise with a clear demonstration of your alignment with the company's core mission. Your interviewers will evaluate you across several distinct dimensions.

Scientific Rigor & Study Design This evaluates your ability to conceptualize, design, and execute robust research methodologies. Interviewers want to see that you can take an ambiguous product or behavioral question, formulate testable hypotheses, and design an experiment or observational study that yields statistically sound conclusions.

Technical Execution & Data Analysis This measures your hands-on ability to manipulate data, run statistical models, and draw insights using industry-standard tools, particularly R. You can demonstrate strength here by writing clean, efficient code and showing a deep understanding of the underlying assumptions behind the statistical tests you choose.

Product Sense & Innovation Betterup looks for scientists who can connect their research to tangible product outcomes. You will be evaluated on your ability to think creatively about how research can improve the user experience, including how new technologies (like generative AI) might be integrated into traditional coaching models.

Mission Alignment & Communication This assesses your cultural fit and your passion for human transformation. Interviewers will look for your ability to communicate complex scientific concepts to non-technical stakeholders and your genuine enthusiasm for the Betterup mission. You must be comfortable navigating a highly mission-driven, sometimes intense corporate culture.

4. Interview Process Overview

The interview journey for a Research Scientist at Betterup is comprehensive and heavily emphasizes both technical execution and cross-functional collaboration. The process typically begins with a conversational screening call with the hiring manager. This initial discussion is often focused on your background, your career aspirations, and your alignment with the company's mission. Do not be surprised if this round leans heavily into behavioral questions and your vision for the future, rather than deep technical probing.

If you advance, you will move into the technical evaluation phases. This usually starts with a live technical screen focused on basic statistics and coding exercises, almost exclusively in R. Following a successful screen, you will be assigned a rigorous take-home data analysis and study design project. This is a critical hurdle in the Betterup process and is known to be highly demanding.

Candidates who successfully pass the take-home stage are invited to a virtual onsite interview. This is an extensive round where you will meet with up to five different cross-functional team members, assessing everything from your deep technical skills to your cultural fit. Finally, because Betterup highly values leadership alignment, the process typically concludes with an interview with a senior executive to ensure your vision matches the company's strategic direction.

This timeline visualizes the progression from your initial hiring manager screen through the intensive technical take-home and final executive rounds. Use this to pace your preparation, ensuring you block out significant uninterrupted time for the take-home assignment, while reserving your energy for the highly collaborative virtual onsite.

5. Deep Dive into Evaluation Areas

To succeed in this process, you must be prepared to demonstrate depth across several core competencies. Betterup interviewers will probe your theoretical knowledge and your practical ability to execute.

Statistical Foundations & Data Analysis

Understanding the mathematical principles behind your work is non-negotiable. Interviewers want to ensure you do not just run functions in a software package, but actually understand the statistical mechanics at play. Strong performance here means confidently explaining the "why" behind your analytical choices.

Be ready to go over:

• Descriptive and Inferential Statistics – Central tendency, variance, hypothesis testing, p-values, and confidence intervals.
• Regression Modeling – Linear, logistic, and mixed-effects models, which are critical for longitudinal behavioral data.
• Assumptions and Diagnostics – How to check for normality, homoscedasticity, and multicollinearity, and what to do when assumptions are violated.
• Advanced statistical methods – Propensity score matching, survival analysis, or advanced causal inference techniques.

Example questions or scenarios:

• "Explain how you would handle missing data in a longitudinal study measuring user well-being over six months."
• "Walk me through the assumptions of a linear regression model and how you would test for them in R."
• "How do you explain a p-value to a non-technical product manager?"

Study Design & Experimentation

Because Betterup relies on evidence-based coaching, your ability to design valid, reliable studies is paramount. You will be evaluated on your ability to structure research that isolates variables and proves efficacy in a noisy, real-world environment.

Be ready to go over:

• Experimental vs. Observational Design – Knowing when to use A/B testing (RCTs) versus quasi-experimental methods.
• Metrics Definition – Translating ambiguous concepts like "resilience" or "leadership growth" into quantifiable metrics.
• Sample Size & Power Analysis – Determining how much data is needed to detect a meaningful effect.
• Survey Design – Best practices for psychometric validation and avoiding bias in self-reported assessments.

Example questions or scenarios:

• "Design a study to determine if a new coaching intervention improves employee retention."
• "What metrics would you define to measure the success of an AI-driven coaching chatbot?"
• "How would you design an experiment if you cannot randomly assign users to the control group?"

Programming & Tooling (R)

Your technical execution will be heavily scrutinized, specifically your proficiency in R. Betterup relies on R for much of its data analysis and statistical modeling. Strong candidates write clean, reproducible, and well-documented code.

Be ready to go over:

• Data Wrangling – Using the tidyverse (dplyr, tidyr) to clean and transform messy datasets.
• Data Visualization – Creating clear, compelling charts using ggplot2 to communicate findings.
• Statistical Implementation – Running models and extracting insights using base R or specialized packages.
• Reproducibility – Structuring your code and using RMarkdown to create shareable reports.

Example questions or scenarios:

• "Given this raw dataset of user interactions, write a dplyr pipeline to aggregate the average session length by user cohort."
• "How would you visualize the distribution of coaching scores across different demographic groups?"
• "Write an R script to run a mixed-effects model accounting for the variance between different enterprise clients."

Mission Alignment & Future Vision

Betterup has a distinct, highly mission-driven culture. Interviewers, especially hiring managers and executives, want to know that you are deeply invested in the concept of human transformation. Strong performance in this area means speaking authentically about your passions and demonstrating how your career goals align with the company's trajectory.

Be ready to go over:

• Personal Motivations – Why you care about coaching, mental fitness, or behavioral science.
• Adaptability – Your willingness to explore new, unconventional research avenues (like testing GPT applications).
• Communication Style – Your ability to engage in deep, sometimes philosophical conversations about human potential.

Example questions or scenarios:

• "Where do you see yourself and your research passions evolving in the next five years?"
• "Tell me about a time you had to pivot your research focus due to changing business priorities."
• "Why are you passionate about the specific mission of Betterup?"

6. Key Responsibilities

As a Research Scientist at Betterup, your day-to-day work will be a mix of deep-focus analytical tasks and highly collaborative strategic planning. You will be responsible for designing and executing behavioral studies that validate the effectiveness of the Betterup platform. This involves taking raw, complex data generated by thousands of coaching sessions and transforming it into clear narratives about human growth.

A significant portion of your time will be spent writing code, primarily in R, to clean data, run statistical models, and build visualizations. You will work closely with the engineering and data infrastructure teams to ensure the data you need is accessible and reliable. You will also partner directly with product managers, translating your scientific findings into recommendations for new features or interventions.

Furthermore, you will act as an internal consultant for innovation. The Labs team frequently explores emerging technologies. You may find yourself designing frameworks to test how large language models (like GPT) can be safely and effectively integrated into the coaching experience. This requires a flexible mindset, as you will balance rigorous, traditional behavioral research with fast-paced, exploratory tech validation.

7. Role Requirements & Qualifications

To be highly competitive for the Research Scientist position, you must bring a strong blend of academic rigor and practical data science experience.

• Must-have skills – Deep proficiency in R (specifically tidyverse and ggplot2); a strong foundation in statistics (hypothesis testing, regression, mixed models); proven experience in end-to-end study design (experimental and observational); and excellent communication skills to translate science to business leaders.
• Must-have experience – An advanced degree (Ph.D. or highly specialized Master's) in a quantitative behavioral science field (Psychology, Cognitive Science, Economics, etc.), plus industry experience applying research methodologies to product or business problems.
• Nice-to-have skills – Familiarity with SQL and Python; experience with natural language processing (NLP) or evaluating LLM outputs; a background in psychometrics or survey validation.
• Nice-to-have experience – Previous tenure at a fast-growing startup; experience working directly with product and engineering teams in an agile environment.

8. Frequently Asked Questions

Q: How difficult is the take-home assignment? The take-home assignment is known to be rigorous and time-consuming. While you may be told it takes 5-6 hours, many candidates report spending significantly longer (up to 15-18 hours) to ensure the code is flawless, the data analysis is deep, and the study design is comprehensive. Plan your schedule accordingly.

Q: Why is there an interview with a senior executive? Betterup places a massive premium on mission alignment and cultural fit. The core team is tight-knit, and executives want to ensure that every senior hire, especially in a strategic role like a Research Scientist, shares the company's vision for human transformation and can thrive in their specific corporate culture.

Q: Will I be tested on Python or SQL? While SQL and Python are valuable, the core technical evaluations for this specific role heavily index on R. You should be fully prepared to write live code and complete your take-home data analysis using R and its associated packages (like tidyverse).

Q: What kind of research does the team actually do? The research blends traditional behavioral science (measuring the efficacy of coaching, psychometric validation) with innovative product testing. Recently, this has included exploring how emerging AI technologies, such as GPT models, can be applied to enhance or scale coaching frameworks.

Q: How fast is the interview timeline? The initial stages (hiring manager screen and technical screen) usually move very quickly, often within a couple of weeks. However, scheduling the virtual onsite and executive rounds can sometimes face delays due to internal startup dynamics and executive availability.

9. Other General Tips

• Timebox but polish the take-home: Because the take-home assignment is a critical differentiator, do not rush it. Even if it takes longer than the suggested time, ensure your R code is impeccably clean, heavily commented, and that your final report reads like a polished internal memo.
• Embrace the mission-driven language: Betterup has a distinct culture that heavily emphasizes pursuing passions and personal growth. Do not shy away from speaking earnestly about your personal mission and why you care about human well-being.
• Prepare for ambiguity: You may be asked questions about applying research to highly ambiguous areas, such as testing GPT chatbots. Show that you can apply rigorous scientific frameworks even to unproven, cutting-edge technologies.

• Brush up on mixed-effects models: Because coaching data involves repeated measures over time nested within individuals (and individuals nested within organizations), mixed-effects models are a staple. Be ready to discuss them confidently.
• Ask probing questions: When given the chance, ask the hiring manager specific questions about the balance between academic research and product-driven analytics. This shows you understand the dual nature of the role.

10. Summary & Next Steps

Joining Betterup as a Research Scientist is an opportunity to apply rigorous behavioral science at an unprecedented scale. You will be instrumental in proving the value of coaching and exploring how new technologies can elevate human potential. The role demands a scientist who is not only technically sharp in R and statistics but also deeply passionate about the mission of human transformation.

This salary data provides a baseline expectation for the compensation associated with this level of scientific expertise. Use it to understand the market rate for the role, keeping in mind that total compensation at a growth-stage startup like Betterup will often include a significant equity component alongside the base salary.

To succeed in this interview, focus heavily on mastering your statistical explanations, polishing your R coding skills for the intensive take-home, and articulating a clear, passionate vision for your career. Remember that your interviewers are looking for a colleague who can navigate startup ambiguity while maintaining scientific integrity. Approach your preparation with focus, leverage resources like Dataford to refine your technical answers, and step into your interviews ready to showcase your expertise. You have the skills to make a massive impact—now it is time to prove it.