In the age of AI, the field of traditional ‘data science’ might seem antiquated or not as important, innovative, or lucrative as emerging technologies.

However, the ebb and flow of tech trends are underpinned by traditional data science methodologies, which means data scientists are still a vital component in the tech ecosystem and will become ubiquitous across all industries as organizations increasingly rely on data to fuel their business objectives.

Arguably, even more so thanks to the advent of generative AI, which has revolutionized our ability to digest vast volumes of data at the touch of a button, but still needs data scientists to interpret and verify AI-gleaned insights into accurate, and actionable assessments.

McKinsey’s latest Global Survey has identified that inaccuracy poses the biggest threat to companies using AI and that investment in analytical AI is now on par with investment in generative AI.

3 data scientist jobs that pay over $100k

• Data Scientist, Advertising, Spotify, New York ($107,766 - $153,951 a year)
• Principal Data Scientist, Salesforce, San Francisco ($185,800 - $296,400 a year)
• Director - Data Scientist Prudential, Newark ($173,500 - $234,700)

Looking to the future

According to a recent study, the data science platform market is forecast to grow to a valuation of $322.9 billion by 2026 at a CAGR of 27.7% during the period of 2021 to 2026.

Understandable when you take into consideration that 147 zettabytes of data has been created so far this year, compared to 120 zettabytes in 2023 and 97 zettabytes in 2022 (it’s estimated that each person can potentially generate around 15.87 terabytes of data daily).

The even better news is that whether you’re a data analyst or data engineer, your skillset and ability to enforce data policies and shape how AI is used is commanding high salaries across the board.

Unlocking your potential

So, how can you pivot to a career in data if you’re already proficient in other areas of tech and are now ready to make the transition mid-career?

For starters, having an understanding of Python, R, and SQL is essential for a career in data science as it will allow you to not only automate tasks but manage structured data, aggregate data, and perform tasks related to machine learning and data visualization.

3 high-paying data scientist jobs

• Data Scientist, Booz Allen, Chantilly ($75,600 - $172,000 a year)
• AML Data Scientist II, TD Bank, Mount Laurel ($95,264 - $155,376)
• Applied NLP Data Scientist, ubs, New York ($150,000 - $200,000 a year)

If you need to brush up on one or all of the above, Udemy is offering a concise (and free) course on data science, machine learning, data analysis, data visualization using Python and R.

Or to get a better understanding of how you can develop your machine learning skills, Andrew Ng, the co-founder of Google Brain and an adjunct professor at Stanford University is running a series of short courses via Coursera.

Whatever you choose, one thing is clear: data science remains one of the most appealing (and generously-paid) career routes in tech.

By Aoibhinn Mc Bride