Where Do Businesses Find Data Scientists?
Data science projects are taking longer than expected, or very unusual that enter in production. We are short of data scientist professionals. Where can we find data scientists?… Well, have you ever thought about internal formation of your existing work force as data scientist?
Most of large organizations are still working to understand what digital transformation is, and some of them even try to implement digital transformation even if they are not very sure about what it is all that about.
What majority of organizations know -at high level at least- is about predictive analytics, even data science.
Among the multiple cause of failure of digital transformation implementation is the lack of a stable data science team. High demand of data scientists means high rotation of data scientist workforce. Rotation cost of those professionals can reach among 60% to 150% of the gross salary (liquidation cost, selection cost, hiring costs, ramping up, etc)
Human resource managers and Data managers usually solve this problem focusing on the following:
1. Acquire best new data science talent
2. Retain best existing talent
3. Keep motivation
Unfortunately, this recipe does not work very well for most of the organizations. So, how to mitigate the high rotation of data scientists?
Source: Quora
Let’s begin analyzing the different type of data professionals available at the market:
1. Newly graduated who learnt data science and machine learning, but no real world experience
2. Experienced data scientist who worked in the industry for a while and has implemented any project in production
3. Any other professional who transitioned to data science at some point in their career
Each category has its pros and cons. A Talent acquisition manager needs to decide when and how to hire a data scientist based on the most immediate need and overall maturity state of the organization with such need.
If the organization is in the middle of a large project and need to replace somebody, very likely will prefer an experienced data scientist with relevant industry experience. This is complex to find and very costly, of course.
In another hand, if the organization is at the beginning of the journey in terms of data analysis maturity, perhaps a good combination of the 3 profiles can be a good approach.
Discovering the hidden data scientist
There is a great source of potential data science talent -usually unexploited or even hidden- in every organization: the existing work force. Have you ever considered your business analysts, your project managers, your IT people, your finance people, accountants, human resource professionals, sales and commercials, business developers, market analysts, etc…?
In case you are any professional but a data scientist, make yourself this question: Do I need data to do my work? Do I need to analyze data to do my work? If your answer is ‘Yes’, then we can say that you are a potential data scientist. All you need is a bit of technical training.
Many organizations have realized about their existing work force as source of data science talent, investing in technical training. The result of this is usually a lot more positive than hiring new data scientists, as they are training individuals that already know the organization culture, the business operations and management style inside-outside.
Just consider what is more feasible -economically and operationally-:
a. To hire high rotation high salary talent
b. To invest in education of your existing talent
Consider that domain knowledge could be even more important that technical skills in data science, so existing human resources already have that.
Benefits of training existing work force in Data Science
In the near-term,
– Improve communication among team members
– Lower need to hire externally
– Ownership sense of existing talent
In the mid-term,
– Lower rotation rate
– Lower hiring cost
– Increase of productivity
– Exponential value added
More DAC Content by Pablo Moreno
How to Interview a Data Scientist
Roadmap to Digital Transformation
Excel or Power BI? … It’s Not About the Tool
Building the Perfect Dashboard
Roles of the Data Professional Clearly Explained
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