Building Data Science Capabilities Means Playing the Long Game

Or a bright, but self-educated graduate who is eager to grow and learn and will seek to further their job skills — even on their own time?6.

Build in a career path that aims to develop data science talentOne strategy that Region’s Bank takes is that it allows and encourages its data scientists and analysts to move around the organization.

It openly discusses and plans for this with its new hires and in annual performance reviews.

So, an entry-level data scientist will come into a department and know that in a few years after learning all there is to learn in that department, they will have the opportunity to move to a new area of the organization where they can continue to develop, contribute and be recognized.

This strategy feeds well into the very simple fact that right about the time your best data scientists are hitting their stride they may also be thinking about starting a family and will thus be looking for new opportunities and challenges within your organization, rather than face a move.

They will likely plan to stay with your company longer when they see that they have a future and that they can start thinking about other life goals.

In addition to this, companies should start creating a hybrid career development track for all employees – one that runs parallel to both data science and the business side, and which allows business people to move into analytics and vice versa.

For example, if your company can afford it, you might put in place education and training programs that support employees who are interested in obtaining an analytics qualification and skill set.

Tuition and fees can be subsidized for these employees in exchange for their agreement to stay with your company for a certain number of years.


Democratize Data ScienceThe real long-term solution to training and developing data scientists comes with democratizing data science.

Companies must find ways to stop relegating data science knowledge to a handful of highly specialized, Ph.


– clad quasi-unicorns.

The responsibility of data science is growing faster than ever.

This puts tremendous strain on both the data science team itself, as well as recruiters and managers.

So what McKinsey, Harvard, Deloitte, and companies like Airbnb have been advocating for is the democratization of data science as a way to both meet the shortage of data science talent, but more importantly, as a critical step toward extracting the true value of data science.

Companies are setting up data science universities to train all management and are hiring “hybrid” roles such as visualization specialists to help bridge the gap between data scientists and the rest of the organization.

Some companies are also actively implementing automated tools, pre-trained models and self-service analytics to make data science techniques and skills more available to the organization at large.

The democratization of data science over time may relieve the pressure on the data science team and promote a fairer judgment of how trained they really need to be.

Admittedly most companies are far from democratizing data science but getting there could go faster than you would think.

Ten Years From Now…It’s clear that there is no single way of meeting the broad array of data science needs in any immediate way without taking a few risks on talent hiring and planning for the future development of data science talent in new ways.

Organizations need to take steps to ease the unrealistic demands now being placed on individual data science candidates and the people trying to hire them.

This requires taking a long-term view toward developing a more balanced set of capabilities within the organization.

Many of the proposed steps don’t really require much in the way of additional investment.

Boot camps and LinkedIn Learning are not all that expensive compared to the cost of recruiting and hiring a “ready-made” data scientist.

Rather what is required is an acceptance that hiring ready-made data scientists is a fading option.

Start working on your long term roadmap now and maybe in 10 years when McKinsey reports that AI has “come of age” you’ll be armed with a team of homegrown and loyal data scientists ready to take advantage of all that the future of data science has to offer.

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