As Hiring Slows, The Drivers Of Demand For Data Scientists Are Changing
Hiring for data professionals is slowing across roles. The hiring freezes are cutting deep. As businesses report lower revenues and slowing growth over the next 90 days, hiring freezes and layoffs will accelerate. However, those who can use technology to deliver value to customers will always have a job.
I am a vocal proponent of data professionals with solid business acumen because I have watched the trend I am about to explain growing across industries and businesses for over a year.
It started in 2016 with Instagram looking for data scientists focused on delivering products vs. research. That spread to most FAANG+ companies by 2017. They talked about innovation in new ways, value-centric and product-centric. That focus spread to the major consulting companies and from there across the business world.
I will use external sources to support this case because I am obviously biased. I have two courses that teach value-centric data science, business acumen, and AI strategy. It's necessary to support this trend with evidence from others, or I will sound like a salesman, not a teacher.
Accenture Survey And A New Narrative
Accenture's survey on the key drivers of AI maturity is the most recent example. According to their survey, businesses don't see returns from data science initiatives until they implement an AI Strategy. Accenture featured AI Strategy as a critical success factor for transitioning from low to high AI maturity. Their survey found that businesses with high AI maturity see over 30% of their revenue produced by data science initiatives.
Their #1 success factor for AI maturity is "Champion AI as a strategic priority for the entire organization." Accenture's #3 success factor is "Industrialize AI tools and teams to create an AI core." Their #5 success factor is "Prioritize long- and short-term AI investments." Accenture emphasizes talent and culture as a critical focus for those seeing the highest revenue gains.
IBM published a study with similar findings. AI strategy and value-centric approaches result in higher maturity and greater returns.
Investors like Jim Breyer, CEO of Breyer Capital, have said, "We invest in technical strategists." Major investors across sectors are parroting that sentiment. They are looking for results. In a previous post, I covered the implications for businesses that need credibility, not just an interesting AI story.
Investors see the difference in results between businesses that can apply models to solve business challenges and those that cannot. Forecasting quality corresponds to decision quality. Supply chain management, customer retention and acquisition, hiring, growth investment allocation, and many other areas show value-centric data science's bottom line impacts.
This paper by the NBER highlights how firms use data to reallocate production and investment to the goods customers want most or those with the highest returns. An increasing body of research detailing the revenue high-maturity businesses are generating from value-centric data science.
These are the drivers for a shift in business focus from technical only to technical strategists. I will use quotes from open job descriptions to highlight what businesses are looking for in new hires and promotions to senior-level roles.
Job Description Tell The Story
The J&J and Zoom quotes show a growing trend; relationship building and partnerships. This requires communication for impact and a deep understanding of external business units.
The Microsoft quote highlights their requirements for data scientists to be capable of connecting business metrics to model metrics. This keeps projects focused on business needs.
The Spotify quote is another trend; the ability to find high-value opportunities to apply data science. Data scientists are expected to have a framework for finding business problems and delivering solutions.
Walmart and Amazon highlight the need for data scientists to communicate with senior leadership. This requires data scientists to understand strategy so they can frame presentations in business terms.
Visa and IBM are creating new revenue streams by connecting data scientists directly with customers. This used to be the sole domain of big consulting, but other businesses have found success with this business model.
Many roles now require business acumen and presentation capabilities for client/customer-facing roles. This growth area for data scientists creates opportunities even as hiring slows in other business units.
Those opportunities and high-end career paths will only be available to data scientists with demonstrable business acumen, communications capabilities, and opportunity identification frameworks.
Wrapping up with Spotify. They summarize the demand for data scientists who combine technical and business capabilities. They are looking for people who can leverage both to deliver value in new ways.
Companies hire and promote high-impact data scientists who can work with teams across the business to deliver value.
Career Implications
Shifts like this do not usually happen this quickly. A perfect storm of forces collides to accelerate the change to business acumen.
Leadership is being cycled to put people with a focus on delivering value into higher-level positions and retraining leaders without it to fit the new paradigm. A New Vantage Partners study showcased how businesses are looking for leaders focused on business growth with a data analytics or data science background. This goes all the way up to C-suite roles like CDAOs.
One side of the data job market is stalling altogether, and another is seeing new talent investments. The market for data professionals who can partner with teams across the business and find new ways to solve business problems or return value to customers is heating up.
This split in the field will create winners and, unfortunately, losers. There is a hesitation amongst some data professionals to embrace the business and soft skills. Those data scientists will be commoditized and lose access to senior-level roles. They will not command the same salaries as today, and layoffs will cut deeper into this group.
Data scientists with the combined capability set will see greater demand, higher salaries, and more opportunities for advancement. This is a safe haven for data professionals, no matter how long the downturn lasts. As FAANG+ and startups scale back hiring, demand will be driven by new industries that need their data science teams to create top and bottom line impacts. They need teams who provide credibility to back up their AI stories. The outlook for data professionals who can meet this need is bright.
when I read
"Identify the most impactful opportunities for
product development based on user needs and execute
on them diligently and resourcetully."
I wonder what product managers are for ) collaboration is a must, but sometimes the boundaries between responsibilities in a team are hard to find, which could be very annoying
Data Scientist is a very abstract role here so I guess your point was just to show that our future on the market and a career path depends on our ability to understand business.