Many companies understand that hiring a Data Scientist can help drive their business forward, but knowing how to manage and best utilise their skills can be challenging. Particularly for founders who have no experience in data or analytics, understanding what Data Scientists do, how long things take, and why they’re important can seem a bit of a mystery. Thus many struggle to realize the full organizational and financial benefits from investing in a Data Scientist or a data science team.
Because founders can struggle to understand the data science world, they often put time and cost saving methods in place that considerably affect the quality of the results from the data, without realising it. It’s up to the founder to be transparent with the Data Scientist about what is needed, and then take the time to really grasp what the Data Scientist can do to achieve that result, so you find the right TCQ balance for your desired outcomes.
The difference between doing this and rushing ahead is the difference between making well-informed decisions, and decisions that have blind spots - as they say, a wrong answer is worse than no answer at all.
We all know that connecting employees to a mission they care about is key to having them produce their best work. Show them the impact of their analyses of decision making, and how they are a valued member of the team. It will lead them to make better products and deliver better services to your customers. Remember that a simple graph may be the result of days of analysis drawn from petabytes of data. What is presented to you is often just the tip of the iceberg in terms of work and it is appropriate to recognize all the work that went into an analysis.
The data science community is large and open, and a cost effective way to upskill your Data Scientist is provide catering and conference room space for them to hold a “lunch and learns” with fellow Data Scientists from other companies. This way they can learn new skills to help better leverage data to the company’s benefit.
It’s important to recognize that data science and engineering go hand in hand. Make sure you’re providing enough engineering support for your Data Scientist to not just do their jobs but thrive at your workplace. If the servers are slow or capacity constrained, it will be difficult for them to iterate quickly, and the result will be a loss of motivation and creativity.
Implementing these points will show your Data Scientist that your company really does care about them and the work they do. And the more engaged they are, the happier they’ll be to stick with you and provide quality work.
This guest post courtesy of The Data Incubator.
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