I know, I know.

The market is in a tough spot. Unemployment started feeling like a full-time job, and it became exhausting.

After all, who signed up to experience burnout before even landing a corporate job? I know I didn't!

As someone who's gone through hundreds of job interviews - both as a freelancer and a contractor- I know what it feels like to think outside the box constantly.

In fact, we're oftentimes so focused on landing a job and being perfect, that we forget about the tiny mistakes we make that much bigger once the stress and anxiety hit us during the interview.

Here are 10 mistakes that are costing you the technical interview pass. I also wrote how to fix them for a better plan of the attack.

1. Skipping the basics and jumping straight to state-of-the-art deep learning

Many candidates think using fancy deep-learning models will impress the interviewer. However, if you don't have a solid understanding of the basics, like linear regression or decision trees, you might struggle to explain why you chose a complex model over simpler alternatives.

This can lead to the interviewer doubting your foundational knowledge and problem-solving abilities.

How to fix this?

Make sure you understand the fundamental algorithms and when to use them. Spend time revisiting core concepts and practicing their applications.

Be prepared to discuss the strengths and weaknesses of basic models and why they might be more suitable for certain problems.

Practice explaining these basics clearly before moving on to more advanced topics.

2. Not writing detailed and explainable README files on GitHub

Your GitHub repository is like your portfolio.

If your README file doesn't clearly explain what your project does, how to run it, and what tools you used, interviewers might think you don't care about documentation or clarity.

This can give the impression that your work is disorganized or hard to understand, which is a red flag for any team.

How to fix this?

Write a thorough README file for each project. Include an introduction, installation steps, usage instructions, and details about the data and methods you used.

Think of it as explaining your project to a friend who isn't a data scientist.

Additionally, include examples of input and output, and highlight any unique features or challenges you addressed. This will show that you value clear communication and attention to detail.

3. Ineffective problem-solving (inability to explain why you chose a certain algorithm)

It's not enough to just pick an algorithm; you need to explain why it's the best choice for the problem at hand. If you can't articulate your reasoning, it might look like you don't understand the algorithms you're using. This can lead to a lack of confidence in your decision-making skills and overall expertise.

How to fix this?

Practice solving problems and explaining your thought process. Focus on the pros and cons of different algorithms and why you chose the one you did.

Think about how you'd explain this to someone new to the field. Consider creating a decision tree or a set of criteria that guides your algorithm selection process, and use it to demonstrate your structured approach to problem-solving.

4. Not asking questions

Interviews are a two-way street. If you don't ask questions, you might miss important details or appear uninterested. It also shows you’re not thinking critically about the problem.

How to fix this?

Prepare a list of questions to ask during the interview. This could be about the company's data infrastructure, the team you'll be working with, or specific details about the problem you're solving.

Show curiosity and engagement. For example, you might ask about the biggest data challenges the company faces or how they measure the success of their data science initiatives.

5. Lack of enthusiasm and seeing the interview as another chore

If you come across as uninterested or bored, the interviewer might think you’re not passionate about the job or data science in general. Enthusiasm can be a deciding factor in hiring, as it often correlates with motivation and a positive attitude toward work.

How to fix this?

Show genuine interest in the company and the role. Talk about what excites you about data science and why you're interested in the position. Smile, make eye contact, and be positive.

Share any personal projects or experiences that highlight your passion for the field. Mention specific aspects of the company’s work that you find inspiring or align with your career goals.

6. Failure to structure problems

Diving into coding or analysis without a clear plan can lead to confusion and mistakes. Structured thinking helps you break down complex problems into manageable parts.

Without this, your approach may seem chaotic, making it hard for interviewers to follow your logic and reasoning.

How to fix this?

Before starting on a problem, take a moment to plan your approach. Outline the steps you'll take and the rationale behind them.

Communicate this structure to the interviewer so they can follow your thought process.

For instance, you might start by defining the problem, then move on to exploring the data, selecting a model, evaluating it, and finally, refining your approach based on feedback.

This systematic approach demonstrates your ability to handle complex problems methodically.

7. Poor communication skills (Difficulty in Interpreting Results and Inability to Explain Methodology)

You might have great technical skills, but if you can't explain your results and methods clearly, it’s hard for others to understand your work or trust your conclusions.

Effective communication is crucial in data science, as you often need to convey complex ideas to non-technical stakeholders.

How to fix this?

Practice explaining your projects and results to non-technical friends or family. Focus on making your explanations clear and concise.

Use visuals or analogies if they help. For example, if you’re explaining a complex model, you might compare it to a simpler, everyday decision-making process.

Additionally, work on your storytelling skills to make your findings more engaging and relatable.

8. Not Considering Data Quality and Preprocessing

Ignoring the quality of your data or skipping preprocessing steps can lead to poor results. Interviewers expect you to consider these steps as they are crucial for any data science project.

How to fix this?

Always check your data for issues like missing values, outliers, and inconsistencies.

Explain the preprocessing steps you take and why they're important. Show that you understand the data's role in the model's performance.

Document your data cleaning process thoroughly, and be ready to discuss any trade-offs or decisions you made regarding data preprocessing.

9. Failure to adapt to Business Context

Technical skills are important, but understanding how your work impacts the business is crucial. If you can’t tie your analysis back to business goals, your work might seem irrelevant.

Data science solutions should always be aligned with the broader objectives of the organization.

How to fix this?

Learn about the company and its industry before the interview. Think about how your data science skills can solve real business problems.

Practice explaining how your projects have delivered business value. For example, you might discuss how a predictive model you developed helped reduce customer churn or how an analysis you conducted led to improved marketing strategies.

10. Lack of Data Structures and Algorithms (DSA) knowledge

Many data science problems require a strong knowledge of data structures and algorithms. If you can't solve these problems efficiently, it might suggest you lack a fundamental skill set.

This is especially important for tasks involving large datasets or requiring optimized solutions.

How to fix this?

Regularly practice DSA problems on platforms like LeetCode or HackerRank. Focus on common algorithms and data structures, and understand their applications in data science.

This will also help you think more critically and efficiently about problem-solving.

Additionally, study how these concepts are applied in real-world data science scenarios, such as optimizing data retrieval from databases or improving the efficiency of machine learning algorithms.

Ace Your Next Technical Data Science Interview

You don't have to decipher the concept of a data science interview by yourself. I created a powerful learning resource that explains and answers over 200 questions commonly asked in data science interviews.

From behavioral and theoretical questions to detailed coding assignments and explanations in Python, SQL, and statistics. In addition to all the technical stuff, there are actionable tips to get you started.

The Data Science Interview Handbook is a limited-time resource and won't always be available. Hurry up and grab yours!

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