Doximity Business Intelligence Interview Questions + Guide in 2025

Doximity is transforming the healthcare industry with its mission to help every physician be more productive and provide better care for their patients.

As a Business Intelligence Analyst at Doximity, you will play a crucial role in enhancing healthcare insights through data analytics. This position involves collaborating with cross-functional teams, including Strategic Analytics, Client Success, and Sales, to create impactful client-facing analyses. You will leverage extensive datasets to identify behavioral patterns of medical professionals and contribute to the development of data products. Ideal candidates should possess a strong proficiency in SQL and Python, as well as the ability to manipulate large datasets and generate actionable insights. A passion for data, curiosity for continuous learning, and a commitment to teamwork and collaboration are essential traits for success in this role.

This guide will assist you in preparing for your interview by focusing on the specific skills and experiences that Doximity values, equipping you with the knowledge to stand out as a candidate.

The interview process for a Business Intelligence role at Doximity is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's mission and values. The process typically unfolds as follows:

1. Initial Phone Screen

The first step is a phone interview with a recruiter or hiring manager. This conversation usually lasts around 30-45 minutes and focuses on your background, experience, and understanding of Doximity's mission. Expect questions about your familiarity with SQL and Python, as well as your approach to data analysis and insights generation.

2. Technical Assessment

Following the initial screen, candidates are required to complete a technical assessment, often in the form of a take-home coding challenge. This assignment typically involves SQL queries and Python programming tasks, designed to evaluate your ability to manipulate data and derive insights. While the company may suggest a time frame of 3-5 hours for completion, many candidates report that it takes significantly longer to produce high-quality work.

3. Technical Interviews

After successfully completing the technical assessment, candidates move on to a series of technical interviews. These interviews usually consist of 2-3 rounds, each lasting about 30-60 minutes. You will engage with team members, including data analysts and engineers, who will delve into your technical skills, problem-solving abilities, and past experiences. Expect discussions around SQL, data visualization, and statistical concepts, as well as behavioral questions that assess your fit within the team and company culture.

4. Final Interview

The final stage often includes a conversation with a senior leader or hiring manager. This interview may cover both technical and behavioral aspects, focusing on your long-term career goals, your understanding of Doximity's impact on healthcare, and how you can contribute to the team. This is also an opportunity for you to ask questions about the company culture and expectations.

Throughout the process, communication is key, and candidates are encouraged to express their curiosity and passion for data.

Next, let's explore the specific interview questions that candidates have encountered during their interviews at Doximity.

Here are some tips to help you excel in your interview.

Prepare for a Structured Process

Doximity's interview process is well-organized but can be lengthy, often involving multiple stages including phone screens, coding challenges, and technical interviews. Familiarize yourself with the structure: expect an initial phone interview, a take-home coding assignment, and several technical interviews. Be ready to discuss your coding assignment in detail, as interviewers will likely ask for your thought process and any challenges you faced.

Master SQL and Python

Given the emphasis on SQL (75%) and Python (3.12%) in the role, ensure you are well-versed in both. For SQL, practice writing complex queries that involve multiple tables and relationships. Brush up on common functions and optimizations. For Python, focus on libraries like NumPy and Pandas, as they are crucial for data manipulation and analysis. Be prepared to discuss your experience with these tools in a practical context.

Showcase Your Analytical Skills

Doximity values candidates who can derive insights from data. Be prepared to discuss past projects where you utilized data analysis to drive decisions. Highlight your ability to ask the right questions and identify key insights from large datasets. This aligns with their core value of curiosity and passion for data.

Emphasize Collaboration and Communication

Doximity's culture emphasizes collaboration and respect. Be ready to discuss how you have worked in cross-functional teams and how you communicate complex data insights to non-technical stakeholders. Prepare examples that demonstrate your ability to collaborate effectively and support your teammates' growth.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your alignment with Doximity's core values, such as reliability, curiosity, and respect. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Prepare specific examples that illustrate how you embody these values in your work.

Prepare for Technical Challenges

The coding challenge is a significant part of the interview process. While the time estimates for these assignments may be conservative, be prepared to invest extra time to ensure your submission stands out. Focus on writing clean, efficient code and include thorough documentation. If you encounter ambiguities in the assignment, note them and be ready to discuss how you approached these challenges during the interview.

Follow Up and Seek Feedback

After submitting your coding assignment or completing interviews, don’t hesitate to follow up for feedback. While Doximity may not always provide detailed feedback, expressing your interest in learning from the experience shows initiative and a desire for continuous improvement.

Stay Informed About Company Culture

Doximity values diversity and inclusion, and they are committed to fostering a supportive work environment. Familiarize yourself with their mission and values, and be prepared to discuss how you can contribute to their culture. Showing that you understand and appreciate their commitment to improving healthcare can set you apart as a candidate.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Business Intelligence role at Doximity. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Business Intelligence Analyst interview at Doximity. The interview process will likely focus on your technical skills, particularly in SQL and Python, as well as your ability to analyze data and communicate insights effectively. Be prepared to demonstrate your understanding of statistical concepts and your experience with data visualization tools.

SQL and Data Manipulation

1. Can you explain the difference between INNER JOIN and LEFT JOIN in SQL?

Understanding the nuances of SQL joins is crucial for data manipulation and analysis.

How to Answer

Discuss the definitions of both INNER JOIN and LEFT JOIN, emphasizing how they differ in terms of the data they return.

Example

"An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. If there is no match, NULL values are returned for columns from the right table."

2. How would you optimize a slow-running SQL query?

Performance optimization is key in handling large datasets.

How to Answer

Mention techniques such as indexing, query rewriting, and analyzing execution plans to improve query performance.

Example

"I would start by analyzing the execution plan to identify bottlenecks. Then, I would consider adding indexes on columns used in WHERE clauses or JOIN conditions. Additionally, I would rewrite the query to eliminate unnecessary subqueries or joins."

3. Write a SQL query to find the top 10 clients by revenue.

This question tests your practical SQL skills.

How to Answer

Outline your approach to writing the query, focusing on the use of GROUP BY and ORDER BY clauses.

Example

"To find the top 10 clients by revenue, I would use a query like: SELECT client_id, SUM(revenue) AS total_revenue FROM sales GROUP BY client_id ORDER BY total_revenue DESC LIMIT 10;"

4. What are window functions in SQL, and how do you use them?

Window functions are essential for advanced data analysis.

How to Answer

Explain what window functions are and provide an example of their application.

Example

"Window functions perform calculations across a set of table rows related to the current row. For instance, using ROW_NUMBER() can help rank sales by revenue within each region without collapsing the data into a single row."

Python and Data Analysis

1. How do you handle missing data in a dataset using Python?

Handling missing data is a common challenge in data analysis.

How to Answer

Discuss various strategies such as imputation, removal, or using algorithms that support missing values.

Example

"I typically handle missing data by first assessing the extent of the missingness. If it's minimal, I might use imputation techniques like filling with the mean or median. For larger gaps, I may consider removing those rows or using models that can handle missing values."

2. Can you explain the use of Pandas for data manipulation?

Pandas is a powerful library for data analysis in Python.

How to Answer

Highlight key functionalities of Pandas, such as DataFrames, and common operations like filtering and aggregating data.

Example

"Pandas allows for efficient data manipulation through its DataFrame structure. I often use methods like groupby() for aggregation and merge() for combining datasets, which makes it easy to analyze complex data relationships."

3. Describe a project where you used Python for exploratory data analysis (EDA).

This question assesses your practical experience with Python in a real-world context.

How to Answer

Provide a brief overview of the project, the data used, and the insights gained.

Example

"In a recent project, I analyzed patient data to identify trends in treatment outcomes. Using Python libraries like Pandas and Matplotlib, I performed EDA to visualize correlations between treatment types and recovery rates, which helped inform our clinical strategies."

4. What libraries do you commonly use for data visualization in Python?

Data visualization is crucial for communicating insights.

How to Answer

Mention popular libraries and their specific use cases.

Example

"I frequently use Matplotlib for basic plotting and Seaborn for more advanced statistical visualizations. For interactive dashboards, I prefer Plotly, which allows for dynamic visualizations that can be shared with stakeholders."

Statistics and Probability

1. What is statistical significance, and how do you determine it?

Understanding statistical significance is vital for data-driven decision-making.

How to Answer

Define statistical significance and explain the role of p-values.

Example

"Statistical significance indicates whether the results of a study are likely due to chance. I determine it using p-values, where a p-value less than 0.05 typically suggests that the results are statistically significant."

2. Explain the concept of p-value.

This question tests your understanding of hypothesis testing.

How to Answer

Discuss what a p-value represents in the context of hypothesis testing.

Example

"A p-value measures the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis."

3. How would you explain the difference between Type I and Type II errors?

Understanding errors in hypothesis testing is crucial for accurate analysis.

How to Answer

Define both types of errors and provide examples.

Example

"A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, concluding a drug is effective when it is not is a Type I error, whereas failing to detect an actual effect is a Type II error."

4. Can you describe a time when you used statistical analysis to solve a business problem?

This question assesses your practical application of statistics.

How to Answer

Share a specific example, focusing on the problem, analysis, and outcome.

Example

"In a previous role, I analyzed customer churn rates using logistic regression to identify factors contributing to churn. The insights led to targeted retention strategies that reduced churn by 15% over six months."