The hunt for the ideal data scientist has begun to monopolize the brain-time of tech thought leaders, company executives, and IT managers. The problem? The hotly-pursued prey is a unicorn, a myth that will lead most companies down an expensive and fruitless trail.

Unravelling the Legend of the Data Science Unicorn

Most companies have a slew of reasons to abandon the search for a data scientist, but the most painfully evident one comes in the form of salaries. Though most salaries are in the range of $150,000 – $200,000, the top data scientists are demanding $400,000 – $500,000, or more!

The New York Times shares a humorous quote on the entire data scientist frenzy: “Salaries are spiraling so fast that some joke the tech industry needs a National Football League-style salary cap on A.I. specialists.”

New data is flowing at unbelievable rates and will continue to do so. The question, then, is how can you make use of the data that is available to you?

Option #1: You can hire one data scientist for $200,000 (assuming you find one who is willing to work at 50% of market value), have him run one iteration after another using the data you have provided, follow up with him three months later to see which output he has ranked as most likely to solve the problem you posed, and then have your experienced, in-house team members integrate the solutions into your current operations.

Option #2: You can engage a data modeling software that relies on artificial intelligence (A.I.), plug in the data, wait a few hours or at most a couple days while the modeling system runs thousands of iterations and ranks the output, and then have your experienced, in-house team members integrate the solutions into your current operations.

A.I. offers beyond-human ability to manipulate and interpret data, and it is without question a critical component of maximizing data. Besides outperforming 10 data scientists, modern technology removes biases inadvertently incorporated into manual iterations by the data analyst. Just as important is the final point of both options detailed above: have your experienced, in-house team members integrate the solutions into your current operations. Those experienced, in-house team members are your domain experts.

Filling Your Business Needs with Domain Experience

Domain experience delivers readily accessible, real-life experience that can be immediately put to work on task and industry-specific problems. One such application is building out a strategy to monetize data (read more here). By equipping non-data experts with the right tools, you enable them to dig in and develop accurate predictive models that can guide your business decisions.

The most effective search for personnel is a combined focus on technology expertise and business experience. Your domain expert might come in the form of a business analyst, an engineer, or another tech-trained professional; the common factor is the ability to learn quickly how to manipulate and apply data to solve problems and guide future business decisions.

Enabling more team members to engage in data analytics is a better choice than equipping a single, costly data scientist who has no business experience. To equip your team, you can leverage the full scope of modern technologies, including cognitive machine learning, predictive analysis, and artificial intelligence (AI). The best way to capitalize on the data available to you is to automate programming, model job functions, and enable your domain experts to access data and analyze it for trends or anomalies.

Business Scenario

I worked recently with a large manufacturing company that was seeking a data scientist. When I laid out the cost, the return-on-investment, and the time to value, the chief information officer was very receptive to hearing about more affordable and effective options. We decided to implement a three-part strategy to meet the company’s data analytics needs.

Step one was to ensure that we had access to a robust collection of data that was ready for analysis, as well as a top-notch, A.I.-based data-modeling platform. We ensured that both pieces were in place, and we were ready to move along with step two of our plan.

Step two was to identify an in-house technical expert who, once equipped with data, would be up and running almost immediately. We selected an engineer who had significant domain experience, and we laid out a plan to bring him up-to-speed in a matter of days.

By equipping a highly capable team with the technology they needed, we were able to eliminate the need for the crippling expense to acquire a single data scientist, who would undoubtedly lack the relevant domain experience. The result was an affordable solution that enabled the company to turn data into an asset (read more here).

Wrap Up

Most companies are in need of experts who can manipulate and make sense of the continuous influx of data, either to manage risk (read more here) or optimize yield (read more here). A warm body or two, however, will not do the job.

Competition among the big boys like Facebook, Uber, and Google have reduced the talent pool and pushed the salary range out of reach for most any other level of company. If by chance you can acquire a data scientist with a more affordable salary, the financial tradeoff is still questionable due to the data scientist’s lack of domain experience. As a result, smaller companies are looking for talent in unusual places: some are overpaying physicists and astronomers, while others are going abroad to round up cheaper labor. Both choices circumvent the importance of domain expertise.

Better options include assembling a team from among existing technical domain experts, or hiring or contracting other talent. Companies have some leeway in the path they select, as long as the focus remains on domain experience and the inclusion of experts who can adapt and quickly add value through predictive data analysis.

So, why keep hunting the unicorn when the most valuable catch is more accessible and affordable? Instead, identify your own business needs, study your options, and then run after the solution that has real substance.


River Point Technology provides technical personnel search and placement services that are vital for companies that need to ease their data analysis burden and capitalize on the power of business intelligence.

River Point lives and breathes data, so who better to provide well-vetted, cutting-edge talent who can advance your business objectives? We partner with every customer to build out the most appropriate personnel search, and then we leverage our real-world experience, industry visibility, and network of more than 1,000 industry relationships to fill your pipeline with the most capable candidates.

River Point Technology’s services help companies access top-notch, data engineers, data analysts, information scientists, data administrators, business intelligence analysts, and data architects. We execute our team fulfillment services through direct placement, contract-to-hire, and contract talent.

We take the complexity out of data.
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