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Linda Burtch, Managing Director at Burtch Works | 30+ years’ experience in quantitative recruiting

Another year – another set of predictions! Last year there were a few notable trends from my 2019 predictions that we touched on throughout the year, including Python overtaking R and SAS in our annual tools survey and an increasing focus on upskilling in our interview with Metis.

It’s been just over a decade since I founded Burtch Works, and so it was quite interesting to reflect on my 2010 forecast which was right in the wake of the economic recession. But as we head into a brand new decade, I’m ready to turn my sights onto what is sure to be exciting years ahead for the data science and analytics fields!

Here are my new predictions for 2020 and beyond:

 

1. The definition of AI will become even more obfuscated

Artificial intelligence is everywhere – book titles, conference topics, project proposals – and as it assumes the mantle of the “next greatest thing”, AI has clearly become the term du jour. The upcoming months will likely see the definition of AI become more broadly defined as more firms attempt to jump in for the sake of keeping up with the Joneses whether they actually have a well-defined need for AI applications or not.

 

2. Innovations and product management roles will be career options for data scientists

As data science continues to mature, I’ve been fielding more requests for candidates who can pivot and use their quantitative expertise in innovation and product management capacities. After all, who better to explore new innovation possibilities or create a data product that can be monetized than a data science expert who is intimately familiar with what data is capable of?

 

3. Upskilling beyond the data team will become the norm

I predict that as firms continue to look for ways to improve enterprise-wide adoption of analytics, the practice of upskilling to increase data literacy throughout different business units will become the norm. Companies have realized that they need to upskill across the organization in order to truly operationalize analytics, and the results of our recent flash survey showed that nearly three-quarters of companies are providing at least some resources aimed at improving data literacy.

4. Data science and analytics hiring in cybersecurity will heat up

Cybersecurity roles include a plethora of positions for quantitative professionals, such as pattern recognition and risk analysis, and as job requirements in this area continue to shift, there has been a persistent hiring gap. While the number of graduates with these specialized skills has been increasing, a report from Burning Glass technologies points out that a significant hiring gap has persisted due to the ever-increasing demand in our overwhelmingly-digital world.

 

5. Salary history bans will have the unintended consequence of increasing salary gaps

For those not already familiar, salary history bans are local or state-wide laws that aim to address pay discrimination by preventing employers from asking potential candidates about their compensation. While the intent is laudable, I am dubious as to whether these bans will have the intended effect. In my opinion, the best route to salary equality is through transparency.

From where I’m sitting, our culture’s salary discussion taboo tends to work out in the employer’s favor, because if no one is discussing their salaries, then it can be harder for employees to see if their salaries are comparable to their colleagues. Examples like the salary spreadsheet that was circulated by Google employees to expose their employer’s pay disparities would seem to suggest that more transparency is better.

Additionally, without salary transparency during the hiring process, higher salaries may simply go to the more aggressive negotiators. Only time will tell what types of consequences these bans will have, but I suspect it will not move us in a more equitable direction.

 

6. There will be more leadership roles with a focus on data ethics and privacy

As I pointed out in Burtch Works’ 10 year anniversary blog post a few months ago, with the increasing spotlight on GDPR, data breaches, privacy, and ethics in data science, there will be a call for analytical leaders who can shepherd firms through this new age. While these roles have existed in some capacity in certain industries such as financial services or healthcare, I suspect they will become even common as more firms look to proactively address ethical concerns.

 

7. We’re headed for a startup shakeout – and data scientists may be caught in the crossfire

Despite some folks who may believe data scientists and analytics professionals are largely exempt from turbulence in the startup market, several recent examples hint that trouble may be brewing on the horizon – and they aren’t immune this go around. I wouldn’t go so far as to say that startup data scientists who get the boot will have trouble finding another job, but with sky-high salaries come sky-high expectations, and if you can’t show employers your value at a startup, you’re likely to be shown the door regardless of your job title.

8. Even your grandparents will be hearing about cryptocurrency

After AI in 2018 and Huawei in 2019, what will your grandparents be discussing at the dinner table this year? My bet is on cryptocurrency! This was a hot industry spotlight in my colleague’s financial services predictions last year, and growing consumer curiosity about things like Bitcoin and Ethereum means even your grandparents may be asking you to explain how blockchain works.

 

9. Data science bootcamp graduates will face increasing scrutiny from employers

With all the froth around data scientist jobs over the past few years (and ahem, their salaries in particular) numerous bootcamps have popped up promising to turn anyone into a data scientist in 12 weeks or even less. However, companies have begun to wise up to the fact that not every bootcamp graduate will necessarily have the data science skills to go along with their fresh completion certificate, so they’ve begun to scrutinize candidate skills more thoroughly. The most successful bootcamps will be the ones who critically evaluate an applicant’s analytical foundation before accepting them into the program.

 

What do you think of my predictions this year? Do you have any of your own that you’d like to add in the comments? Let me know what you think!

Want to learn more about my predictions and their implications for quantitative careers and employers? You can view the 30-minute webinar recording below, where I discuss these trends in more detail.

 

Trackbacks/Pingbacks

  1.  2018 Predictions for the Analytics & Data Science Hiring Market - Burtch Works
  2.  2019 Predictions for the Data Science & Analytics Hiring Market - Burtch Works
  3.  2017 Predictions for the Analytics & Data Science Hiring Market - Burtch Works
  4.  Predictions for the 2016 Analytics & Data Science Hiring Market - Burtch Works
  5.  2020 Data Science & Analytics Q&A with Linda Burtch: Growth Areas, Startup Shakeout, Data Translators, and More - Burtch Works

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