To succeed at Grow Therapy, you must understand exactly what the team is looking for in each core competency. Let's break down the primary evaluation areas.

SQL & Analytical Engineering

This is the foundation of the Data Scientist role at Grow Therapy. Because the data team is lean, you will often act as your own analytics engineer, writing the queries and building the data models that power your analyses.

Be ready to go over:

• Complex Joins and Aggregations – Knowing when to use inner, left, outer, or self-joins, and how to aggregate data across different dimensions.
• Window Functions – Mastery of functions like ROW_NUMBER(), RANK(), LEAD(), and LAG() to analyze sequential user behavior.
• Data Modeling (ETL) – Understanding how to structure raw transactional data into clean, reusable star-schema or snowflake-schema tables for business intelligence.
• Advanced concepts (less common) – Query optimization techniques, indexing strategies, and handling nested JSON data within SQL databases.

Example scenarios:

• "You have a table recording every status change for a therapist's application. Write a query to find the average time spent in each stage of the onboarding funnel."
• "Optimize a query that calculates rolling 7-day active users across a massive clickstream dataset."

Product Sense & Metric Validation

At Grow Therapy, data is used to drive product decisions. You must demonstrate that you do not just calculate numbers, but that you understand the underlying business mechanics and can validate your findings against skepticism.

Be ready to go over:

• Metric Frameworks – Designing North Star metrics and supporting input metrics for new product launches.
• A/B Testing & Experimentation – Understanding sample size calculation, statistical significance, and how to handle network effects in a two-sided marketplace (therapists and patients).
• Data Validation & Quality Assurance – Proactively identifying data anomalies, tracking down tracking errors, and ensuring your metrics are not "made up."

Example scenarios:

• "We launched an update to our search algorithm, and bookings increased by 5%. How do you prove this increase was caused by the algorithm change and not external factors like seasonal hiring or marketing spend?"
• "Define the success metrics for a new portal that allows therapists to manage their clinical notes more efficiently."

Data Analysis & Python/R Execution

Your ability to take a messy dataset, perform exploratory data analysis, and extract a compelling narrative is highly valued. You will be evaluated on your programming structure, code cleanliness, and visualization choices.

Be ready to go over:

• Exploratory Data Analysis (EDA) – Using pandas, numpy, or tidyverse to clean, filter, and summarize data.
• Data Visualization – Creating clear, intuitive charts (using matplotlib, seaborn, or ggplot2) that highlight the core insights without cluttering.
• Statistical Modeling – Applying regression models or basic statistical tests to identify correlation and trends.

Example scenarios:

• "Using the provided CSV of therapist availability, analyze the relationship between a therapist's calendar open slots and their ultimate booking rate. Visualize your findings."
• "Explain how you would handle missing values in a patient feedback dataset before running a sentiment analysis."

As a Data Scientist at Grow Therapy, your daily work will span multiple domains, ensuring that data is reliable, accessible, and strategically impactful.

• Analytics Engineering & ETL – Design, build, and maintain robust data pipelines and dbt models to transform raw data into clean, analytical datasets.
• Product & Business Partnership – Collaborate closely with Product Managers and Operations leads to define key performance indicators (KPIs), design experiments, and evaluate feature launches.
• Exploratory Analysis – Conduct deep-dive analyses to uncover opportunities for improving therapist retention, patient matching success, and billing operational efficiency.
• Dashboarding & Reporting – Build and maintain self-serve BI dashboards (using tools like Looker or Tableau) to empower non-technical teams to monitor their own metrics.
• Data Governance – Ensure data quality and consistency across the organization by establishing clear definitions and documentation for core business metrics.

Grow Therapy looks for candidates who possess a strong blend of technical execution and product-mindedness.

• Must-have skills –

  • Advanced SQL proficiency (writing complex queries, window functions, and performance optimization).
  • Strong programming skills in Python or R for data analysis, cleaning, and visualization.
  • Demonstrated experience with ETL/ELT pipelines and data warehousing concepts.
  • Strong product sense and experience defining, tracking, and validating product metrics.
  • Excellent verbal and written communication skills, with a proven ability to present data insights to non-technical stakeholders.

• Nice-to-have skills –

  • Experience working with dbt (data build tool) and modern data stack tools.
  • Background in healthcare technology, HIPAA-compliant data environments, or two-sided marketplaces.
  • Familiarity with basic machine learning concepts and predictive modeling.
  • Prior experience as the founding or early data hire in a fast-growing startup.

Q: How technical is the Data Scientist role at Grow Therapy? A: It is highly technical but leans heavily toward analytics engineering and product analytics rather than advanced machine learning or AI research. You need to be an expert in SQL, comfortable writing Python/R, and capable of building clean data models.

Q: What is the take-home assessment like? A: The take-home is a comprehensive, three-part test consisting of five SQL questions, one data analysis question, and one data visualization question. You are typically asked to use Python or R for the analysis and visualization portions. While recruiters suggest spending limited time on it, most successful candidates report spending 2 to 3 hours to ensure high-quality, well-documented submissions.

Q: How does the team evaluate product thinking? A: Interviewers will ask you to explain how you measure impact, design experiments, and validate metrics. They want to see that you do not take data at face value and can think critically about user behavior and business logic, especially in a two-sided marketplace context.

Q: Is there opportunities for remote work? A: Grow Therapy operates with a highly flexible, hybrid-friendly culture, though specific location requirements can vary depending on the team and the level of the role. Be sure to clarify current expectations with your recruiter during the initial screen.

To maximize your chances of success during the Grow Therapy interview loop, keep these practical tips in mind:

• Upsell your ETL and analyst abilities: While it is great to have machine learning experience, the immediate business needs at Grow Therapy often center around clean data pipelines, reliable metrics, and actionable product insights. Frame yourself as a highly capable analytics partner who can build their own data infrastructure if needed.
• Document your take-home assumptions: When submitting your take-home code, include a clean README file. Document your assumptions, explain why you chose specific visualization styles, and outline how you would scale your analysis if you had more time. This demonstrates professionalism and structured thinking.
• Be ready for metric pushback: During the panel interviews, expect interviewers to ask probing questions about your analytical choices. If you present a metric, be ready to explain exactly how you calculated it, how you validated its accuracy, and how you ruled out bias or confounding variables.
• Show passion for the mission: Grow Therapy is dedicated to improving mental health accessibility. Showing a genuine interest in the healthcare space, therapist enablement, and patient care can significantly set you apart from other highly technical candidates.

The Data Scientist position at Grow Therapy is an exceptional opportunity to leverage your analytical skills to make a tangible, positive impact on the mental health care system. By combining technical rigor in SQL and Python with strong product intuition and clear data storytelling, you can position yourself as an invaluable asset to this rapidly growing team.

As you prepare, focus on mastering complex SQL queries, refining your experimentation frameworks, and practicing how you present analytical insights to cross-functional partners. Remember to approach the take-home assessment with diligence and structured documentation, as it is a crucial milestone in the hiring process.

To gain further insights into compensation expectations, check out the salary data below:

The salary module displays the typical compensation ranges for data roles at this level. Use this data to benchmark your expectations and guide your discussions during the final stages of the process. For more comprehensive interview preparation resources, real candidate reviews, and practice questions, explore the additional guides available on Dataford. Good luck with your preparation—you have all the tools you need to succeed!