Doximity is at the forefront of transforming the healthcare industry, facilitating better productivity and care through its expansive network, which is the largest for medical professionals in the United States. Joining Doximity as a Data Analyst means contributing to this impactful mission by analyzing data insights and enhancing healthcare solutions.

As a Data Analyst, you will engage with cross-functional teams comprising analysts, engineers, and product managers. Your role involves utilizing extensive datasets to identify key behavioral patterns, creating insightful client-facing analytics, and continually honing your technical skills in SQL and Python.

To aid your preparation, Interview Query will guide you through the interview stages, including phone interviews, coding challenges, and technical rounds focusing on SQL, Python, and statistical analysis. Dive in to make your mark in this dynamic healthcare ecosystem.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Doximity as a data analyst. Whether you were contacted by a Doximity recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Doximity Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Doximity data analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Doximity data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Doximity's data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, discussing a real piece of Python script and SQL whiteboard coding may also be a part of the interview.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Doximity office or virtually. Your technical prowess, including programming and modeling capabilities, will be evaluated against other candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data analyst role at Doximity.

Quick Tips for Doximity Data Analyst Interviews

• Prepare for a Lengthy Process: The process includes various stages, starting from an initial phone screen to technical rounds and concluding with an onsite interview or multiple rounds with the hiring managers and product managers. Being patient and well-prepared for each stage is crucial.
• SQL & Python Mastery: Ensure your SQL and Python skills are up to par. The technical round will likely focus on your ability to write complex SQL queries and your understanding of Python for data analysis.
• Behavioral Questions: Apart from technical skills, being prepared for behavioral and situational questions can give you an edge. These questions often delve into your experiences working with teams, handling deadlines, and managing projects.

Typically, interviews at Doximity vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Also, mention the time complexity of the function.

• Write a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. The complexity should be (O(n)).

• Write a function precision_recall to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array and return its index, or -1 if the value is not found. The algorithm's runtime complexity should be (O(\log n)).

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

• How would you set up an A/B test for button color and position changes? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What steps would you take if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What actions would you take to investigate and address this issue?

• Why might job applications be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common issues in "messy" datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss the advantages of using random forest compared to logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier model? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to evaluate the model's accuracy and validity?

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything fishy about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect anything unusual about these results?

• How do you find the median in a list with more than 50% repeating integers in O(1) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in O(1) computational time and space. Example input: li = [1, 2, 2]. Example output: median(li) -> 2.

• What are the drawbacks and formatting changes needed for messy datasets? Assume you have data on student test scores in one of the given layouts (dataset 1 and dataset 2). Identify the drawbacks of the current organization, suggest formatting changes for better analysis, and describe common problems in messy datasets.

What is the average salary for a Data Analyst at Doximity?

Q: What is the interview process for a Data Analyst position at Doximity? The interview process includes the following steps: an initial phone interview, a coding test assignment (typically SQL and Python tasks), technical interviews (covering SQL and Python), and interviews with the hiring manager and product manager focusing on statistical knowledge and behavioral aspects.

Q: How should I prepare for the SQL and Python assessments at Doximity? To prepare for the SQL and Python assessments, practice coding exercises on Interview Query, focusing on writing and understanding complex SQL queries and performing Exploratory Data Analysis (EDA) using Python. Ensure you are comfortable with libraries like pandas, numpy, and matplotlib.

Q: What kind of projects will a Data Analyst work on at Doximity? Data Analysts at Doximity will work on a variety of projects, including creating client-facing analyses, developing data products from scratch, automating code for reuse, and leveraging extensive datasets to identify and classify behavioral patterns of medical professionals.

Q: What skills are essential for a Data Analyst role at Doximity? Essential skills for this role include excellent SQL skills, proficiency in Python for EDA, a strong understanding of statistical concepts, and the ability to present data to non-technical audiences. Data visualization and previous experience in the healthcare industry are also valuable.

Q: What is the company culture like at Doximity? Doximity values diversity, collaboration, and continuous learning. The team is built on mutual respect and reliability, and the company promotes an inclusive culture where all employees are encouraged to bring their full, authentic selves to work. The work environment is dynamic, supportive, and focused on making a direct impact on the healthcare system.

Doximity offers a promising opportunity for data analysts looking to make a significant impact on the healthcare industry. The interview process is thorough, involving multiple stages including SQL and Python challenges, behavioral assessments, and technical evaluations. While some candidates have reported a less-than-smooth experience, others have found the interviews to be an excellent showcase of their skills. If you are passionate about transforming healthcare through data and want to be part of a dedicated team, this role could be a great fit for you. For comprehensive preparation, explore our Doximity Interview Guide where we cover potential questions and detailed insights into the interview process. At Interview Query, we equip you with the tools and confidence needed to excel in your interviews. Check out all our company interview guides for enhanced preparation. Good luck with your interview!