Top Data Scientist Interview Questions: Technical & Non-Technical

Introduction

Data science jobs are predicted to rise by 35% by 2032, way faster (on average) compared to all occupations. Put simply, data scientist is one of the most in-demand career opportunities in India today, and also across the world. Businesses are struggling with a colossal volume of data around; they need skilled data scientists and analysts to help them extract valuable insights to empower them with data-driven decisions. Growing demand for data scientists and related career domains have led to cut-throat competition in the job sector. Thus, apart from enrolling in the best data science course online, you also have to stay updated about the most asked interview questions.
This blog sheds light on some commonly-asked data scientist interview questions (both tech and non-tech) by interviewers and the answers they are looking for.

Technical Data Scientist Interview Questions

Here is a list of probable technical questions that will help with your data science interview preparation.

Q1: What separates supervised learning from unsupervised learning?

Answer: Supervised learning involves training a model using labelled data, while unsupervised learning deals with unlabeled data and finding patterns or structures within it.


Q2: Explain the bias-variance tradeoff?

Answer: The bias-variance tradeoff refers to the tradeoff between a model's ability to fit the training data (low bias) and its ability to generalise unseen data (low variance). A model carrying high bias would underfit. On the other hand, a model carrying high variance would overfit.


Q3: Suppose there is some missing data in your dataset. How would you handle the situation?

Answer: This is one of the common data science questions asked in interviews. Missing data can be handled through techniques such as imputation (replacing missing values with estimated values) or deletion (removing rows or columns with missing data).


Q4: What is regularisation and why is it important?

Answer: Regularisation is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function. It helps in controlling the complexity of the model and improves its generalisation ability.


Q5: What is the purpose of cross-validation?

Answer: Cross-validation is used to assess the performance of a model on unseen data. In this case, the data available is split into several subsets, the model is trained on some subsets, as well as evaluated on the remaining subset. It helps in estimating the model's performance and detecting potential issues like overfitting.

Non-Technical Data Scientist Interview Questions

Here is a list of probable non-technical data scientist interview questions.

Q1: How do you approach a complex data science problem?

Answer: When faced with a complex problem, I start by understanding the problem domain and defining clear objectives. Then, I gather and explore the data, identify relevant features, and choose appropriate algorithms. I iterate through the modelling process, validate the results, and communicate findings effectively.


Q2: Can you explain a challenging data science project you worked on?

Answer: Here you can share details about a project that highlights your problem-solving skills, teamwork, and ability to handle challenges effectively. Describe the project's objectives, the techniques you used, any obstacles you encountered, and the final outcomes or insights gained.


Q3: How do you stay updated with the latest trends in data science?

Answer: I actively participate in online communities, attend webinars and conferences, read research papers, and follow influential data science blogs and thought leaders. Additionally, I take part in online courses or certifications to enhance my skills and stay up-to-date with the latest tools and techniques.


Q4: How do you communicate technical findings to non-technical stakeholders?

Answer: When communicating with non-technical stakeholders, I focus on presenting the results in a clear, concise, and visually appealing manner. I avoid jargon and use data visualisations or storytelling techniques to convey complex concepts. I ensure that I understand the audience's background and tailor the communication accordingly.


Q5: How do you manage multiple projects and tight deadlines simultaneously?

Answer: I prioritise tasks based on their importance and urgency, create a well-structured plan, and break down the work into smaller manageable chunks. I also communicate effectively with team members or stakeholders to set realistic expectations and ensure a smooth workflow. Time management, multitasking, and effective prioritisation are crucial in handling such situations.

Conclusion

Remember that these are just probable data science interview questions and answers; the actual interview questions may vary depending on the company and interviewer. It's essential to thoroughly understand the concepts and techniques related to data science to perform well in interviews. Make sure to enroll in a reputed data science training institute that offers both theoretical and practical training to empower you for a real-world workplace.
We wish you all the very best.