Charles River employees use SQL to extract and analyze experiment data, helping them evaluate the effectiveness of new drug treatments and make research more efficient. They also manage databases for various research projects, ensuring data is accurate and easily accessible, this is the reason why Charles River includes SQL questions in interviews for Data Science, Data Engineering, and Data Analytics roles.
So, to help you study, we've curated 8 Charles River Laboratories International SQL interview questions – able to solve them?
Assume you are a data analyst at Charles River, a leading financial technology firm. You are provided with a dataset representing trades made by various clients. Each record represents a distinct trade featuring attributes such as , , , and . Your task is to write a SQL query to calculate the rolling 3-day average trade volume per security.
trade_id | client_id | trade_date | security_id | volume |
---|---|---|---|---|
8502 | 25 | 2022-09-01 | 345253 | 200 |
8301 | 32 | 2022-09-02 | 231445 | 50 |
8123 | 12 | 2022-09-02 | 345253 | 100 |
7814 | 45 | 2022-09-03 | 423526 | 75 |
7205 | 12 | 2022-09-03 | 345253 | 150 |
6300 | 76 | 2022-09-04 | 231445 | 200 |
5981 | 23 | 2022-09-04 | 423526 | 100 |
date | security | avg_volume |
---|---|---|
2022-09-03 | 345253 | 150 |
2022-09-04 | 231445 | 125 |
2022-09-04 | 423526 | 87.5 |
This query calculates the rolling 3-day (inclusive of current day) average trade volume for each security. It uses the window function with to segment data by and to sort data by from oldest to latest. specifies the range of rows used in the calculation of the average for each row - the current row and the two preceding rows for each security (3-day window).
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
Explore the recent announcements from Charles River that highlight their commitment to advancing drug discovery and development! Keeping up with Charles River's news can help you appreciate their role in supporting the life sciences sector.
Imagine there was a table of Charles River employee salaries, along with which department they were in. Write a query to compare the average salary of employees in each department to the company's average salary. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
Write a SQL query for this question directly within the browser on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department Salaries.
To discover records in one table that are not present in another, you can utilize a and filter out any values in the right-side table.
For example, say you had a table of Charles River customers and a 2nd table of all purchases made with Charles River. To find all customers who did not make a purchase, you'd use the following
This query returns all rows from the customers table, along with any matching rows from the purchases table. If there is no matching row in the purchases table, values will be returned for all of the right table's columns. The clause then filters out any rows where the purchases.id column is , leaving only customers who have not made a purchase.
You are a data analyst at Charles River, a firm providing products and services for drug discovery tasks. There are two primary tables you have access to: and . The table has a and fields. The table has , , , and fields. Your objective is to calculate the average transaction amount per customer.
customer_id | name |
---|---|
1 | John Doe |
2 | Jane Doe |
3 | Mary Johnson |
transaction_id | customer_id | transaction_date | amount |
---|---|---|---|
5001 | 1 | 2022-08-01 | $50 |
5002 | 2 | 2022-08-02 | $75 |
5003 | 2 | 2022-08-03 | $125 |
5004 | 3 | 2022-08-01 | $100 |
5005 | 3 | 2022-08-02 | $200 |
5006 | 1 | 2022-08-02 | $60 |
5007 | 1 | 2022-08-03 | $70 |
This PostgreSQL query will join the and tables on the field and then group the data by customer. For each customer, the query calculates the average amount of their transactions using the function. The results are returned in a new set with customer details along with their average transaction amount.
The final output from this query will look like:
customer_id | name | avg_transaction_amount |
---|---|---|
1 | John Doe | $60 |
2 | Jane Doe | $100 |
3 | Mary Johnson | $150 |
To practice a very similar question try this interactive Uber User's Third Transaction Question which is similar for requiring analysis on users' transaction data or this Stripe Repeated Payments Question which is similar for involving transaction amounts.
A constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had Charles River employee data stored in a database, here's some constraints you'd use:
In the Charles River employee example, the constraint is applied to the "email" field to ensure that each employee has a unique email address. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two employees had the same email address.
Charles River, a digital product company, would like to analyze its click-through-to-purchase conversion rates. The company records every event when a user clicks an advertisement (), views a product page (), and adds a product to their cart (). Calculate the click-through-to-purchase conversion rate for each advertisement.
Provided are three sample tables in the markdown format: , , and .
click_id | user_id | click_date | ad_id |
---|---|---|---|
1 | 21 | 06/08/2022 00:00:00 | 101 |
2 | 35 | 06/10/2022 00:00:00 | 102 |
3 | 45 | 06/18/2022 00:00:00 | 103 |
4 | 21 | 07/26/2022 00:00:00 | 104 |
5 | 33 | 07/05/2022 00:00:00 | 105 |
view_id | user_id | view_date | ad_id | product_id |
---|---|---|---|---|
1 | 21 | 06/08/2022 01:00:00 | 101 | 201 |
2 | 35 | 06/10/2022 05:00:00 | 102 | 202 |
3 | 45 | 06/18/2022 08:00:00 | 103 | 203 |
4 | 21 | 07/26/2022 12:00:00 | 104 | 204 |
5 | 33 | 07/05/2022 15:00:00 | 105 | 205 |
add_id | user_id | add_date | product_id |
---|---|---|---|
1 | 21 | 06/08/2022 02:00:00 | 201 |
2 | 35 | 06/10/2022 00:00:00 | 202 |
3 | 45 | 06/20/2022 00:00:00 | 203 |
4 | 21 | 08/01/2022 00:00:00 | 204 |
5 | 33 | 07/10/2022 00:00:00 | 205 |
This PostgreSQL query calculates the click-through-to-purchase conversion rates for each advertisement by dividing the count of unique cart_add_ids by the count of unique ad_click_ids. The is used to connect all tables based on and the relevant identifiers ( and ). This ensures that all ad clicks are considered, even if they did not result in a product view or cart addition.
To practice another question about calculating rates, try this TikTok SQL Interview Question within DataLemur's online SQL coding environment:
The clause is used to remove all duplicate records from a query.
For example, if you had a table of open jobs Charles River was hiring for, and wanted to see what are all the unique job titles that were currently available at the company, you could write the following query:
Write a query that retrieves customer information alongside their purchases and outputs a list showing customer name, purchase date, purchase ID, and the product name they purchased. Join the customers table with the purchase table on the customer id, and the products table on the product id. Only return rows where the purchase date is after January 1, 2022.
customer_id | first_name | last_name |
---|---|---|
101 | Maria | Davis |
102 | James | Johnson |
103 | Linda | Miller |
104 | Robert | Rodriguez |
105 | Patricia | Garcia |
purchase_id | customer_id | purchase_date | product_id |
---|---|---|---|
201 | 101 | 02/10/2022 | 301 |
202 | 102 | 01/15/2022 | 302 |
203 | 103 | 03/23/2022 | 303 |
204 | 104 | 02/28/2022 | 304 |
205 | 105 | 12/20/2021 | 305 |
product_id | product_name | category_id |
---|---|---|
301 | iPhone 12 | 1 |
302 | Samsung Galaxy S21 | 1 |
303 | MacBook Pro | 2 |
304 | Dell XPS 13 | 2 |
305 | iPad Pro | 3 |
The above query first joins the table with on , and then joins the resulting table with on . It then selects the necessary columns and only includes records where the is after January 1, 2022.
Because joins come up frequently during SQL interviews, try an interactive SQL join question from Spotify:
The best way to prepare for a Charles River SQL interview is to practice, practice, practice. Beyond just solving the earlier Charles River SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Microsoft, Google, Amazon, and tech startups.
Each problem on DataLemur has multiple hints, full answers and best of all, there is an online SQL code editor so you can instantly run your SQL query and have it checked.
To prep for the Charles River SQL interview you can also be useful to solve SQL questions from other healthcare and pharmaceutical companies like:
In case your SQL skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this free SQL tutorial.
This tutorial covers topics including handling timestamps and aggreage functions like MIN()/MAX() – both of which pop up often in Charles River interviews.
Beyond writing SQL queries, the other topics covered in the Charles River Data Science Interview are:
I think the optimal way to study for Charles River Data Science interviews is to read the book Ace the Data Science Interview.
It has 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google). The book's also got a refresher on Python, SQL & ML. And finally it's helped a TON of people, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.
While the book is more technical in nature, it's also important to prepare for the Charles River behavioral interview. A good place to start is by reading the company's values and mission.