
As the leading online fresh food delivery platform in the United States, theInstacart The technical team of Instacart pays a lot of attention to the product landing ability and the actual ability to solve problems, so in the interview process, not only examining the algorithmic skills, but also pays a lot of attention to the system design, product understanding, behavioral ability, belonging to the kind of tech company that "can't be penetrated by brushing the questions purely." We Programhelp recently assisted several students to complete the SDE interview to the final interview. Recently, we assisted a few students to complete the SDE face-to-face interview of Instacart, and today, we will combine the real experience to restore the whole process + high-frequency questions to help you get ready for the interview.
Instacart SDE Interview Process Overview
Instacart's DS interviews are more difficult overall, especially focusing on experimental design and statistical reasoning, and favoring product-oriented technical talents.
The complete process is as follows:
- HR Phone Screen: Understand the candidate's background + job fit, ask about motivation, project experience, etc;
- The first round of technical interview (Product Sense): a business scenario is given, requiring the definition of key metrics and the design of experimental programs;
- Second round of technical side (Stats + SQL): A/B test result interpretation, P-value application + basic SQL query;
- Onsite Ring::
- Take-Home Assignment: an experimental data analysis assignment that requires a report to be written and presented;
- SQL Programming: Statistics on sales, etc. based on rating stars;
- Product Analysis Question: Analyze the Busy Pricing strategy;
- Experimental design question: designing an advertising/promotion experiment;
- Project presentation: Report on take-home;
- HR Closing face.
Instacart SDE High Frequency Problem Types Explained
Experimental design and statistical inference
What are the implications if the t-test violates the assumption of independence? How is the diagnosis made?
Given two experiments: one is A vs B and the winner is then compared to C, and the other is all three at the same time. Which one is better? Why?
With 20 separate functions saving 5 seconds each, PM thinks it will save 100 seconds in total. What do you think?
The focus is on examining whether the "summation fallacy" (e.g., Simpson Paradox) or multiple comparison bias can be recognized.
SQL Query Basics
The average of the sales volume is counted according to the rating scale;
Identify outlier data with unusual order quantities;
Count the change in click-through rates from each promotion.
The SQL questions were overall of medium difficulty, examining clear logic and data cleansing skills.
Take-Home Challenge Analysis
A typical topic is A/B test data analysis. The data contains multiple funnel steps, and you need to dissect the churn rate for each step, not just the final conversion rate.
Key Recommendation:
Draw the complete conversion funnel;
Pay attention to the dropout at each step;
Clearly recommend conclusions and suggest optimizations in the context of the business.
Instacart SDE Programming Challenge Questions Review
Challenge #1
Write an in - memory, key - value store that can "time travel." This can build off of Challenge #2.
Support 'fuzzy' matching on a timestamp.
kv = KV.new
timestamp = kv.set('foo', 'bar')
sleep(1)
kv.set('foo', 'bar2')
# The case of no timestamp should continue to return the latest set value
kv.get('foo')
=> "bar2"
# Fetch the key 'foo' with the initial timestamp, plus 750 milliseconds
kv.get('foo', timestamp + 0.75)
=> "bar" # returns the closest set value to that timestamp, but always in the past
Challenge #2
Write an in - memory, key - value store that can "time travel." This can build off of Challenge #1
If a timestamp is provided for a given key, fetch the value that was set at that exact time for that key. If no timestamp is supplied, fetch the most recently If a timestamp is provided for a given key, fetch the value that was set at that exact time for that key.
kv = KV.new
timestamp = kv.set('foo', 'bar')
sleep(1)
kv.set('foo', 'bar2')
# Fetch the key 'foo' with the correct timestamp
kv.get('foo', timestamp)
=> "bar"
# Fetch the key 'foo' without a timestamp
kv.get('foo')
=> "bar2" # returns the last set value
Task Description
The min, max, average value and count of valid data on each day for each sensor to stdout. The average for each day is the mean of all valid values observed.
Sample Output
Output should include a header and a line for each date, station, sensor combination.
date,sensor_id,min_value,max_value,avg_value,count
2020 - 05 - 15,00001,10.00,55.00,30.00,10
2020 - 05 - 15,00002,20.00,60.00,42.50,20
2020 - 05 - 16,00001,30.00,70.00,45.00,15
Field Formats:
date is a date string. Default timezone.
sensor_id same as above
count is number valid samples observed
min_value, max and avg_value floating point value. Printed with the format "%0.2f"
No spaces before or after commas on any line.
Tips
- Working code and quality are important.
- Write something simple, well structured, easy to understand first, even if it has issues.
- Refine your code, fixing issues as time permits.
Log File Format
The first few lines of every log file will have a header following by a CSV line with 3 columns.
sensor_id, value, timestamp
00001, 11.11, 1589567888
00002, 12.1, 1589567889
Field Formats::
sensor_id is a 5 digit integer
value is a floating point number
timestamp is unix timestamp. the time the reading was taken.
Potential Data Issues::
There may be duplicate readings for a specific sensor. (line is exactly the same). Ignore them.
There may be "bad" data. Fields that don't conform to the format above.
Assignment
Given one of these log files, write a program that reads the file and outputs the min, max, average value and count of valid data on each day for each sensor to The average for each day is the mean of all valid values observed.
Instacart Interview Tips Advice
Instacart's DS/ML job interviews are not only about the ability to brush up on questions, but also about whether you have the analytical thinking and business understanding in the landing scenarios. Here are a few areas to focus on:
A/B testing fundamentals should be solidFamiliarize yourself with the experimental design process and understand the common misconceptions about the independence hypothesis and hypothesis testing, and know how to respond scientifically to a PM who makes a statement such as, "20 features each boosted by 5 seconds = total boost of 100 seconds."
SQL Focus on logical clarity of expressionThe test questions are mostly practical business analysis, you need to master join, group by, window function and other core syntax, it is recommended to do more SQL questions practice in the field of e-commerce.
Product analysis should have a sense of framework: When encountering a product problem, first sort out the goal and then disassemble the indicators, the common structure is: goal → user behavior → indicator selection → evaluation method.
Take-home assignments are analyzed in modulesClear structure is the first thing, and it is recommended to present it in "background-analysis-conclusion-recommendation", while paying attention to the completeness of data preprocessing, visualization, and reasons for recommendation.
Programhelp Help you get an Instacart Offer
We are a team that specializes in interview coaching for technical positions, and have successfully assisted several students to pass Instacart, Meta, Stripe, Amazon, and other companies.
There was a student who majored in statistics + internship experience in e-commerce, and had no idea about A/B test and Product sense in his first interview with Instacart. He contacted us a week before the interview, and we customized an onshore solution for him, and finally he got the offer through Onsite.
If you are also preparing for Instacart's OA or Onsite, feel free to private message us for exclusive materials and one-on-one coaching services.