The Interview Query 2024 Data Science Report: The Rise of AI Jobs

The Interview Query 2024 Data Science Report: The Rise of AI Jobs


In this 2024 data science interview and job market report, we analyze the state of the job market and interviews over time. Our goal is to answer the following questions.

  • What is the current status of tech layoffs, and what are the possible causes?
  • How have companies changed their interview processes since COVID-19 and the slowdown of the job market?
  • How do data scientists and engineers prepare for the disruption caused by AI and LLMs?
  • Is there a slowdown to begin with?

All of these questions are discussed below. You can also refer to our 2021 State of the Market Report for additional references.

Our Summary of Findings

Tech layoffs peaked in early 2023 and decreased after early-year cuts at Amazon, Alphabet, and Microsoft. However, layoffs saw an uptick at the start of 2024, likely due to tiered layoffs.

Several key factors, such as over-hiring, outsourcing, and interest rates, have augmented job layoffs in the tech industry. However, the job market remains strong.

AI-related jobs and industries are expected to grow by 40% year over year, creating over a million jobs by 2027. We’ve also seen that machine learning and AI engineering job postings have significantly increased.

Data scientist salaries have plateaued since 2021, but early reports for 2024 indicate an upward trend.

On the other hand, machine learning engineer salaries have risen recently due to the increasing demand for AI products.

Companies have shifted from behavioral to technical interview questions, focusing more on algorithms, SQL, and machine learning. Major tech companies now emphasize practical technical skills and structured behavioral assessments.

Are We Losing Our Jobs to AI? No.

Despite the unemployment rate remaining below 4.0% since January 2022, the tech industry has been plagued by high unemployment rates due to several key factors.


The national average for unemployment in the United States hovers around 3.4% in 2023.

In the first months of the year, Amazon faced significant layoffs, with 18,000 in January and 9,000 in April. Similarly, Alphabet (Google’s parent company) laid off 12,000 employees at the start of the year. Microsoft also began the year with 10,000 layoffs.

The driving force behind this surge in tech unemployment has been the rising interest rates in the US. Interest rates skyrocketed from a mere 0.08% in 2022 to an imposing 5.33% in April 2024, representing an astronomical increase of over 6800%. This rise significantly impacts the tech industry, which heavily relies on investment. Higher interest rates make borrowing more expensive, leading to a decline in investment and, consequently, a wave of layoffs.

Additionally, market analysts suggest that stock prices play a crucial role. Restructuring and layoffs often lead to favorable responses from investors, boosting stock prices as companies appear to streamline operations and cut costs.


There is a positive linear relationship between stock prices and layoffs.

Tech Is Bouncing Back

Amidst all that has been happening, one question has kept industry insiders wondering: could data science professionals bounce back from this?

Many industry experts believe that the recent layoffs are a result of overhiring during the tech boom of 2021. As companies aimed to rapidly expand, they ended up with redundant positions, leading to the current correction phase.

Interestingly, despite the ongoing layoffs, the overall number of employees being let go has significantly decreased since January 2023, with a reduction of around 90%.

By mid-September 2023, FAANG employment levels had dipped to 2.7%. Although layoffs rose in January 2024, unemployment levels had once again dipped by March of the same year.


Is Machine Learning the New Cool Kid on the Block?

The World Economic Forum predicts a 40% growth in AI-related fields, resulting in over a million new jobs. Additionally, the AI industry itself is expected to see substantial growth, with the machine learning segment alone projected to reach a value of $355 billion by 2027.


The data above shows the ratio of machine learning-related job postings in Interview Query compared to all job postings. As you can see, there is a year-on-year increase in the ratio and a particularly sharp one in 2023.

In fact, not only is machine learning engineering seeing a spike, but check out the increase in job openings in AI research!


Note: We also covered this in our September 2023 Data Science Job Report. Check it out if you want to read more on the topic.

Data Science’s Death Was Greatly Exaggerated

We’ve heard the news: data science is plateauing, and the demand has been more stable these past few years. Well, according to recent news, that’s not necessarily the case. While it is true that both data analytics and data science suffered a salary downturn during 2023, recent 2024 data show that data science has reached a new high.

Moreover, the future is even more promising for machine learning engineering, with sharper salary increases despite the 2023 slump. Machine learning engineer salaries have not slowed since 2022 and are expected to grow in 2025.


Despite 2023’s salary slump, the data industry is experiencing new salary highs in 2024.

It’s Not Just Salaries—Jobs Are Coming Back

It’s not just machine learning that’s seeing an upward trend. According to recent reports, data science job postings, along with data engineering, data analytics, and machine learning engineering and research, are bound to reach new highs in 2024.


All data-related jobs are seeing an upward trend in job postings in 2024. However, machine learning jobs are not seeing that much of an uptick—simply because they never had a slump in the first place. Data science jobs, the most heavily affected field during the slump, experienced a 96.21% increase in job postings when comparing the start of 2023 to the end of 2023.

Have companies shifted their interview processes due to recent events?

We’ve analyzed over 180,000 interview experiences and compared them to previous years. We’ve found that, yes, companies have shifted from asking behavioral questions to technical ones.

The interview processes at companies like Meta, Apple, Amazon, Netflix, Google, and Microsoft—often referred to collectively as MAANG+Microsoft—have undergone significant transformations over the past few years.

Looking back, it seems that the most prevalent questions companies asked were behavioral in nature. Questions such as:

These were the most likely questions you would encounter.

But comparatively, database design questions have been on the rise, with approximately 20–25% of recent interview experiences discussing database design.

Machine learning and data engineering questions have also become more prominent, covering topics like how to explain neural networks, designing ETL pipelines, etc.

Here’s a clearer look at how each company has adjusted its approach from 2021 to now:



In 2021, interviews were fast-paced, with few behavioral questions, a strong emphasis on product and analytics, and very difficult technical reviews, resulting in positive feedback.

By 2024, there is a greater focus on algorithms and behavioral questions while still emphasizing statistics. The process remains fast, with quick transitions between stages. Communication remains crucial, and reviews are now more moderate.

This shift reflects a broader, more balanced approach to candidate assessment.



In 2021, interviews were tough, optimization-focused, and lengthy, often receiving negative reviews. By 2024, interviews have become more coding-centric, with whiteboard sessions and generic questions, resulting in more positive reviews.

The emphasis on algorithms remains strong, while other areas like product cases, probability, and machine learning are less emphasized.

This shift reflects a move towards a simpler, coding-focused interview process.



In 2021, there was a strong emphasis on machine learning and leadership behavioral questions. By 2024, the focus has shifted to more algorithms, coding challenges, and take-home assignments, with behavioral questions using the STAR method.

Machine learning remains important, while product cases, probability, SQL, and statistics are less emphasized.

This reflects a move towards practical technical skills and structured behavioral assessments.



In 2021, Netflix interviews typically began with a technical phone call, focusing on the candidate’s background and resume.

These interviews were quite difficult. By 2024, the interview process has become even longer, potentially lasting up to 12 rounds, while still remaining difficult.

The emphasis on algorithms remains consistent, with less focus on product cases, probability, machine learning, SQL, statistics, and A/B testing.

This suggests a continued emphasis on assessing technical and problem-solving skills through a rigorous and extended interview process.



In 2021, Google interviews used Google Docs for technical coding, with easy machine learning questions, and were generally perceived as easier than expected, being good, transparent, and enjoyable.

By 2024, the focus has shifted to more difficult and advanced algorithm questions and a significant emphasis on SQL. Machine learning questions are still not very prevalent.

This indicates a move toward more challenging technical assessments, particularly in algorithms and SQL, while maintaining a moderate focus on other areas like product cases, probability, and statistics and A/B tests.

A Greater Importance to Algorithms

During the initial years of data science, most data scientists were analytics machines. They tinkered around with databases to generate insights, performing EDA and data mining.

However, with the rise of analytics processes involving machine learning and with more and more data scientists able to create small data pipelines by themselves, algorithms have moved to the forefront of data science interviews.

We can also see this trend in “self-reliant” data scientists with the rise of system design questions. Typically, these questions involve designing databases and machine learning systems and creating APIs, which involve complex and moving parts.

Meanwhile, product and case study questions seem to have taken a bit of a dive.


Data Structures and Algorithms cement themselves as an integral foundation that most data scientists should concern themselves with.

Job layoffs only tell one side of the story—learn more about our success stories.

Alma worked as an actuarial analyst for four years but is now a data analyst at Lyft after just 3 months of applying. Dania spent seven years in HR but has successfully transitioned to data science. Simran applied to over 4,200 jobs before landing her role at JP Morgan Chase & Co.

These individuals are just a few of the many we’ve helped over the years. They showcase the successes of those who have used Interview Query, even in the face of layoffs and a challenging job market.

These success stories not only describe how these individuals started, the progress they made along their journey, and the steps they took to achieve success but also serve as a reminder that no matter how challenging things may appear at present, it is only temporary.

Layoff Predictions and How to Prepare for Them

The only way to prepare for the inevitable rise of AI is to embrace it.

Focus on strengthening your AI skills and mastering the right tools and frameworks to leverage AI effectively. Stay updated with the latest AI news and trends.

AI isn’t going away anytime soon and is here to stay. By focusing on these areas, you can effectively prepare as AI becomes integrated not just in various industries but also in everyday life.

Final Notes

Many companies lack effective plans to support employees during layoffs, showing they’re unprepared for managing job cuts. At Interview Query, we ensure you’re well-prepared for your next interview.

Layoffs are inevitable, but with our resources—including interview questions, take-home assignments, coaching, and mock interviews—you can confidently handle company interviews despite recent challenges.

For more insights into data science jobs, check out the links below: