Data Scientist Interview Guide

The online assessment typically takes 30 minutes to 1 hour and includes coding challenges. Its primary purpose is to help companies screen out candidates who lack the minimum technical skills or are simply not the right fit for the role. 

Candidates are usually required to complete the assessment on a coding platform like HackerRank. For instance, you might be asked to write a Python program to solve a specific problem.

The first phone screen is with a recruiter which usually lasts about 30 minutes and includes behavioural and cross-functional questions. During this short call, the recruiter will discuss the role you've applied for, the company's culture, and your preferences. For instance, they may ask about your past experiences in similar roles and inquire about your salary expectations. It's also an opportunity for you to ask questions about the company and clarify any doubts you have.

This one typically lasts between 30 minutes to 1 hour where you're interviewed by a Data Scientist or the hiring manager. It usually starts with a brief introduction, where you might be asked to talk about yourself and your previous experiences.

Following the introduction, the interviewer will delve into technical questions. The specific questions can vary based on the role you're applying for and may encompass topics like data extraction using SQL, metrics, statistics, probability, machine learning, and coding questions. 

In most cases, you'll face a maximum of two types of questions tailored to the role. For example, if you're seeking an Analytics role, you might receive a combination of a data extraction question and a metrics question. Since the interview content can vary significantly, it's a good idea to talk to your recruiter to gain insights into what to expect before the interview.

During this round, you are typically given  2 weeks to work on a specific case and provide recommendations based on provided data. The nature of the problem can vary widely, from exploratory analysis to simply cleaning data or extracting insights (this typically involves SQL). For instance, you could be given a dataset from the company's sales records—and asked to analyse it and create a presentation with insights on sales trends. Some candidates may be asked to write a detailed report instead.

You can expect more difficult cases revolving around modelling or engineering as well.

You are allowed to use various tools like SQL, Python, or R to complete the task.

After completing the case study, you'll usually have to present your findings to the interview panel. This is your opportunity to explain your thought process and the rationale behind your recommendations.

This one's the final hurdle before that job offer—and mind you, it's comprehensive. Here's what you can expect in most onsite interviews

• Technical Interview: Be ready for a mix of questions, from data extraction to computer science fundamentals. Brush up on data structures, algorithms, and SQL. Seriously, SQL skills are crucial no matter where you're applying.
  
• Case Analysis: You'll dive into real-world scenarios and discuss how you'd tackle them—it could be anything from diagnosing metric shifts to evaluating product features. Be ready to brainstorm metrics and solutions.
  
• Probability & Statistics Interview: This one's all about applied statistics and probability, like experiments and Bayes' theorem. Make sure you're solid in this area.
  
• ML Interview: Expect questions on ML fundamentals and problem-solving with common techniques.
  
• Behavioural Interview: This is where you’ll talk about past experiences with the hiring team—questions are mostly non-technical, focusing on how you handle common situations.
  
• Culture-fit Interview: This one's about fitting in. Share examples of your past collaborations and your approach to hypothetical scenarios, aiming to show you're a good fit for the company's culture.
  

It might seem a bit unusual, but the most effective method is to articulate your responses as if you were conversing with the interviewer face-to-face. Speaking aloud allows you to truly gauge how your answers will come across, aiding in practising your tone, pace, and non-verbal communication.

For an even more rewarding experience, consider partnering with a mock interviewer for practice, preferably someone in the data science field at your target company—you can find many such resources on Prepfully.

The more you engage in this practice, the more effortlessly your answers will flow, and the better equipped you'll be to recall the information during the actual interview.

• Explain the concept of cross-validation in machine learning and its importance in model evaluation.
• Describe a time when you had to work with messy, real-world data and the steps you took to clean and preprocess it.
• Distinguish between bias and variance in the context of machine learning model performance.
• Describe the Random Forest classifier and its key characteristics.
• Explain the scenarios in which you would use L1 regularisation versus L2 regularisation in machine learning
• Discuss the trade-offs between model interpretability and predictive accuracy in machine learning. When would you prioritise one over the other?
• What is feature engineering, and why is it crucial in machine learning? Share an example of a feature engineering task you've undertaken.
• In a time series analysis, how would you handle missing data points or outliers, and what impact can they have on your results?
• Explain the concept of a p-value and confidence interval in simple terms, as if explaining it to a 10-year-old.
• Calculate the probability of a person being infected with a disease given a positive test result, assuming a 0.1% disease prevalence.
• Determine the mean and variance of a binomial distribution.
• Explain the bias-variance trade-off in model selection. How do you strike the right balance between underfitting and overfitting?
• In a time series analysis, how would you handle missing data points or outliers, and what impact can they have on your results?
• Explain the bias-variance trade-off in model selection. How do you strike the right balance between underfitting and overfitting?
• Describe the differences between supervised and unsupervised learning in machine learning. Provide real-world use cases for each.
• Using a dataset of purchasing behaviour, analyse the conversion rate trend and provide actionable suggestions for its improvement.
• Write SQL code to calculate the average purchases for each user.
• Find the median revenue grouped by country without relying on the median or percentile functions in SQL.
• Implement a Python code to calculate the median of an unsorted array.
• Code a classical machine learning algorithm, such as k-means or k-nearest neighbour, using Python.
• How would you assess the performance of a recommendation system, and what metrics would you use to measure its effectiveness?
• Share your experience in deploying machine learning models to production. What challenges did you encounter, and how did you address them?
• Discuss the role of regularisation techniques like Ridge and Lasso in linear regression.
• Describe a situation in which your recommendation conflicted with someone else's. Explain how you reached a resolution.
• Discuss a time when you had to prioritise multiple high-urgency projects and explain how you managed the situation
• How would you design an experiment for X feature?