8 Regeneron SQL Interview Questions (Updated 2024)
Regeneron employees write SQL queries for analyzing complex biological datasets to uncover insights that drive innovative drug development and improve treatment outcomes. They also manage patient records for drug research trials, ensuring that all relevant data is organized and accessible for analysis, this is why Regeneron covers SQL problems in interviews for Data Science, Analytics, and Data Engineering jobs.
Thus, to help you prepare, we've curated 8 Regeneron Pharmaceuticals SQL interview questions – how many can you solve?
8 Regeneron Pharmaceuticals SQL Interview Questions
SQL Question 1: Average Patients Assessment Scores
As a data scientist at Regeneron, you are tasked with conducting an analysis of data coming from health clinics that use Regeneron's medication. Each clinic sends quarterly assessment scores of patients using Regeneron's medication. The dataset contains the clinic ID, the patient ID, the assessment quarter and year, and the assessment score.
Write a SQL query to calculate the running average of assessment scores for each clinic over the quarters and years.
The result should include the clinic_id, year, quarter, the total number of assessments so far, and the running average of the assessment scores (rounded to 2 decimal places).
Example Input:
| clinic_id | patient_id | assessment_year | assessment_qtr | assessment_score |
|---|---|---|---|---|
| 1 | 101 | 2020 | 1 | 77 |
| 1 | 102 | 2020 | 1 | 73 |
| 1 | 103 | 2020 | 2 | 80 |
| 1 | 104 | 2020 | 2 | 79 |
| 2 | 201 | 2020 | 1 | 85 |
| 2 | 202 | 2020 | 2 | 90 |
| 2 | 203 | 2020 | 3 | 87 |
| 2 | 204 | 2020 | 3 | 89 |
Answer:
This query uses window functions to calculate running counts and averages over the assessment scores for each clinic. The clause groups the data by , while the clause sorts it within each clinic by and . The operand calculates the running totals from the beginning of each partition to the current row, while the does the same for the moving averages. The function is used to round the averages to two decimal places. Finally, the clause in the outer query orders the results.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
Explore Regeneron's innovative technology and scientific advancements that are driving progress in the biopharmaceutical industry! Understanding Regeneron's efforts can help readers appreciate the importance of research and development in improving health outcomes.
SQL Question 2: Department vs. Company Salary
You're given a table of Regeneron employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
You can solve this interview question and run your code right in DataLemur's online SQL environment:
The answer is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department vs. Company Salary.
SQL Question 3: Can you describe the difference between a correlated and a non-correlated sub-query?
A non-correlated sub-query is not linked to the outer query and can be run on its own. It does not reference any columns from the outer query and is used to retrieve additional data for the outer query. On the other hand, a correlated sub-query is one that relies on the outer query and cannot be run independently. It uses the outer query to filter or transform data by referencing a column from it, while the outer query uses the results of the inner query.
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Regeneron customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Non-correlated sub-queries are typically faster, as they only have to be executed once, wheareas correlated sub-queries are slower since they have to be re-evaluated for each row of the outer query.
SQL Question 4: Filter Customer Records
As a data analyst in Regeneron, you are handed the task to analyze customer data. Your task is to write an SQL query that retrieves customer information based on specified conditions: customers who are aged 25-45, live in California and have a subscription to the Oncology department.
Here is an example of the data contained in the table:
Example Input:
| customer_id | first_name | last_name | age | state | subscribed_department |
|---|---|---|---|---|---|
| 3221 | John | Doe | 35 | CA | Oncology |
| 2736 | Jane | Smith | 27 | NY | Cardiology |
| 8654 | Sam | Brown | 40 | CA | Oncology |
| 9534 | Amy | Johnson | 50 | CA | Oncology |
| 5742 | Bill | Williams | 32 | TX | Dermatology |
Your output should look like this:
Example Output:
| customer_id | first_name | last_name | age | state | subscribed_department |
|---|---|---|---|---|---|
| 3221 | John | Doe | 35 | CA | Oncology |
| 8654 | Sam | Brown | 40 | CA | Oncology |
Answer:
This query filters the table using a clause to satisfy the three conditions stated in the question. The operator is used to filter the customers with age 25 to 45, and the '=' operator checks for customers living in 'CA' and subscribed to 'Oncology' department.
SQL Question 5: How do the SQL commands and differ?
No, in almost all cases, and for all practical purposes, and do NOT produce the same result.
While both are similar, in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.
SQL Question 6: Average Medicine Sales
Regeneron is a biotechnology company that produces numerous medicines. As a data analyst, one of your tasks could be to calculate the average sales in dollars of each individual medicine on a monthly basis. Given the tables and , where represents the sales transactions of medicines and contains the details about the medicines, write a SQL query to calculate the average sales per medicine for each month.
Example Input:
| sales_id | medicine_id | sale_date | sale_amount |
|---|---|---|---|
| 1 | 101 | 06/08/2022 | 5000 |
| 2 | 102 | 06/10/2022 | 4000 |
| 3 | 101 | 06/18/2022 | 6000 |
| 4 | 101 | 07/26/2022 | 7000 |
| 5 | 102 | 07/05/2022 | 3000 |
Example Input:
| medicine_id | medicine_name |
|---|---|
| 101 | Medicine A |
| 102 | Medicine B |
Example Output:
| month | medicine_name | avg_sale_amount |
|---|---|---|
| 6 | Medicine A | 5500 |
| 6 | Medicine B | 4000 |
| 7 | Medicine A | 7000 |
| 7 | Medicine B | 3000 |
Answer:
This query utilizes the function to calculate the average sales per medicine for each month. It first joins and tables on . Then it uses the clause to group the result by month and . The average sales amount is then calculated for each group. The result is ordered by month and to make it easier to read.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top grossing items or this Amazon Average Review Ratings Question which is similar for calculating average value per item over time.
SQL Question 7: How do you identify records in one table that aren't in another?
To locate records in one table that are absent from another, you can use a and then look for values in the right-side table.
For example, say you exported Regeneron's CRM (Customer Relationship Management) database, and had a table of sales leads, and a second table of companies.
Here's an example of how a query can find all sales leads that are not associated with a company:
This query returns all rows from the sales leads table, along with any matching rows from the companies table. If there is no matching row in the companies table, values will be returned for all of the right table's columns. The clause then filters out any rows where the column is , leaving only sales leads that are NOT associated with a company.
SQL Question 8: Locate Clients with 'NY' in their Addresses
Regeneron operates globally, but many of its clients are located in New York State. In this question, you are asked to identify the clients whose address contains the string 'NY'. Use the SQL keyword to filter down the client database to only those records that match this condition.
Example Input:
| client_id | name | address |
|---|---|---|
| 1 | John Doe | 123 Main St, Albany, NY |
| 2 | Jane Smith | 456 Oak Rd, San Francisco, CA |
| 3 | Sara Johnson | 789 Broadway, New York, NY |
| 4 | Michael Brown | 321 Pine St, Los Angeles, CA |
| 5 | Emily Davis | 654 Maple Ave, Buffalo, NY |
Your task is to return a list of and of the customers who live in New York('NY' is present in their address).
Answer:
This query will filter the table to include only those clients whose address contains the string 'NY'. The % sign is a wildcard character that matches any sequence of characters. By putting % on both sides of 'NY', the query will match addresses that contain 'NY' anywhere in the string.
Example Output:
| client_id | name |
|---|---|
| 1 | John Doe |
| 3 | Sara Johnson |
| 5 | Emily Davis |
Regeneron SQL Interview Tips
The best way to prepare for a Regeneron SQL interview is to practice, practice, practice. Beyond just solving the above Regeneron SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Netflix, Airbnb, and Amazon.
Each DataLemur SQL question has multiple hints, detailed solutions and most importantly, there's an online SQL code editor so you can easily right in the browser your SQL query and have it graded.
To prep for the Regeneron SQL interview you can also be a great idea to solve SQL problems from other healthcare and pharmaceutical companies like:
However, if your SQL skills are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this free SQL tutorial.
This tutorial covers things like removing NULLs and math functions – both of which pop up routinely during Regeneron SQL interviews.
Regeneron Pharmaceuticals Data Science Interview Tips
What Do Regeneron Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems to prepare for the Regeneron Data Science Interview include:
- Statistics and AB Testing Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- ML Modelling Questions
- Behavioral Interview Questions based on Regeneron culture and values
How To Prepare for Regeneron Data Science Interviews?
To prepare for Regeneron Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from companies like Google, Tesla, & Goldman Sachs
- a crash course on Stats, ML, & Data Case Studies
- over 1000+ 5-star reviews on Amazon
Also focus on the behavioral interview – prepare for that using this list of common Data Scientist behavioral interview questions.