Amazon Web Services (AWS), as we all know, is a subdivision of Amazon that is known to provide on-demand cloud computing platforms. With its data-dependent services, AWS always has a hot demand for Data Scientists.
Cracking a Data Scientist role at AWS is no child’s play. The technical competencies you’ll be evaluated on include:
- Problem solving
- Data analysis and manipulation
- Machine learning / AI
- Business acumen
A Data Scientist interview at AWS is indeed difficult. However, a good thing is that with proper preparation, your chances of being offered a job at Amazon Web Services increase significantly.
The first step to preparing for a Data Scientist interview is understanding the interview process followed by practicing popularly asked questions. In this article we share with you a step-by-step guide of Data Scientist interviews at AWS.
With this article you will have a fair idea about how the Data Scientist interviews take place at AWS along with some commonly asked questions to prepare.
Let’s get started.
How are the rounds set for a Data Scientist Interview at Amazon Web Services?
Here is a smooth sailing roadmap for each interview round.
Round-1 Recruiter Screening
Once after applying, the recruiter will directly contact you over the phone and have a brief screen call. This round will mainly focus on your background, a discussion about the interview rounds ahead and a walk through of the job description.
The recruiter or hiring manager may not be someone from a technical background. Therefore, few of the questions can be:
- Could you tell us about yourself?
- What made you choose AWS?
- Where do you see yourself in the next 5 years?
Pro Tip: This is the best time to ask specific questions about what to expect and what to prepare for the subsequent rounds.
Round-2 Technical Screening
As per the interviewer, you may have an at home assignment, a video call with live coding, a call focused on machine learning, or a combination of two of these.
These home assignments will consist of a coding assessment or a case study for you to explore in-depth. You may even be asked to present your case study in the second stage of your technical screen, or during one of the onsite interview rounds.
Pro Tip: Be prepared to answer machine learning questions and to work out SQL and Python/R questions on a shared notepad document.
While other tech giants focus only on technical skills, Amazon takes special interest in your past experiences. Therefore make sure to explain your past projects properly and if there have been any business issues that were solved by you.
Pro Tip: Detailing concrete steps and framing them in the context of the leadership principles is a smart move.
Round-3 Onsite Screening
Once you are through with the technical round, the next step is to spend a day for an onsite interview at the Amazon office. This round may consist of 5-6 loops of face to face interviews one of which can be an informal interview over lunch.
Each loop will last for approximately 45-60 minutes and will consist of interviewers from different departments. Senior executives, managers, data scientists and someone from the designated team will hold the interviews separately.
Pro Tip: Make sure to answer with composure, because you may also be interviewed by an executive known as the ‘Bar Raiser’ who focus on overall candidate quality rather than specific team needs.
The format of the interviews may vary, but will consist of case studies, technical presentations, Q&As, whiteboarding, or otherwise. Your recruiter should provide you with information on what to expect before going in.
20 Expert proven questions that are frequently asked
Here are 20 questions that will help you prepare for for you interview at AWS:
- Describe different JOINs in SQL.
- There are 4 red balls and 2 blue balls, what’s the probability of them not being the same in the 2 picks?
- What is cross-validation?
- Write a Python function that displays the first n Fibonacci numbers.
- How do you inspect missing data and when are they important?
- Write a SQL code to explain month to month user retention rate.
- What is the most advanced query you’ve ever written?
- How would you explain hypothesis testing for a newbie?
- Given a bar plot, imagine you are pouring water from the top. How do you qualify how much water can be kept in the bar chart?
- How would you improve a classification model that suffers from low precision?
- When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?
- What is lstm? Why use lstm? How was lstm used in your experience?
- What did you use to remove multicollinearity? Explain what values of VIF you used.
- Explain different time series analysis models. What are some time series models other than Arima?
- How does a neural network with one layer and one input and output compare to a logistic regression?
- We have two models, one with 85% accuracy, one 82%. Which one do you pick?
- If given an integer n and an array of numbers, give out the histogram divided into n bins.
- Write a python code for recognizing if entries to a list have the same characters or not. Then what is the computational complexity of it?
- Given a csv file with ID and Quantity columns, 50million records, and the size of the data is 2gig, write a program to aggregate the QUANTITY column.
- Given a table with three columns, (id, category, value) and each id has 3 or less categories (price, size, color); how can you find those id’s for which the value of two or more categories matches one another?