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7 Tips On How To Transition Internal Employees To Data Science Team

7 Tips On How To Transition Internal Employees To Data Science Team


The need for data scientists is real — companies across the world are taking significant steps to onboard the best talents from the domain. However, that is not an easy task — whether it’s the talent crunch or the hiring process itself.



In an attempt to grow their Data Science team, companies are undertaking upskilling programmes which have gained popularity in the last few years. To plug in the demand, the trend of internal training and onboarding has gained traction. Companies are upskilling their employees to fill the void in their workforce. And this concept can also be implemented in filling the void in an organisation’s data science department.

In this article, we are to take a look at some of the points that companies can consider in order to help employees make a transition into data science.

Shortlist The Best Fit Candidates

When you decide on to help an employee make that transition into data science, you need to give him/her the platform to upgrade skill-set. This can be done by gauging the level of interest and aptitude. At first, you need to figure out who all are interested in making that transition and also who all fits perfect at data science.

So how can talent managers do that? One of the best ways to filter candidates is by having an in-dept presentation or meeting about the domain. The presentation should cover each and every aspect of the data science domain — be it the paycheck, the advantages, the disadvantages, the challenges, future growth, how they can surmount the hurdles etc. Be transparent to your employees and let them decide whether they want to stick to their current role or make that transition.

You can also refer to our article “4 Tips On How Data Scientists Should Conduct Meetings Successfully” that would definitely help you make your meetings more effective.

Once you shortlist the candidates, the next step should be to check which employees are the best-fits. And you can do that by organising a test or maybe by having a one-on-one interaction with the candidate.

Word to the wise: The entire process of upskilling an employee for the data science team is going to cost the organisation a significant chunk of investment and time. So, make sure you do not compromise with the potential of a candidate — make sure the candidate you have shortlisted are dedicated and are ready to work hard and learn.

Put In Place An Effective Training Schedule

Training is one of the most crucial phases in the entire process of helping an employee from a different domain move to data science. So, how can a company do this without making the employee feel stressed when s/he is also working on its current role?

To make training more effective and less stressful, companies need to first reduce the current workload of the employee, giving the candidate enough room to learn. For example, if a developer is making a transition to data science, a company can let him/her work the first half of the day on its current job role and the second half of the day, he/she can attend the training sessions.

Data Science is a vast domain, it needs a lot of focus and dedication to master. So, when you are learning the nuts and bolts of data science, you cannot afford to be distracted. Therefore, make sure that the developer is not assigned major projects that would consume a lot of his time and make him feel stressed out.

Provide Relevant Learning Resources

Data Science is a vital part of an organisation and the professionals working on that domain has to well versed with each and everything. And for that, the training quality and learning resources have to be best-in-class.

Office-hour data science training is definitely imperative; however, the candidate might also require doing some homework and for that, the company must provide any kind of resources that are required (whether paid or free).

Also, resources include infrastructure or ecosystem where the candidate can gain hands-on in order to have the practical knowledge. So, if you already have a team of data scientists, you can always ask them to help the candidate in case of anything.

To know more about data science roles and learning resources, you can also visit the education section of our website.

Manage Time

One of the most crucial things about the entire process is the time it takes. Many companies does not agree in investing a lot of time and hence tend to either skip sessions or concepts or topics or they just trim the training program.

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But that doesn’t work effectively all the time. Instead of trimming and skipping, companies can decide the data science job role they want the candidate to work on, and according to the job role and responsibilities, a customised training program can be created. It would not only serve the need but would also save a lot of time.

Conduct Tests

Training is definitely important, but when you are putting in so much money and time to upskill an employee, you need to keep a track of how that employee is performing. Conduct tests to check the progress of the candidate. The test can be conducted based on time, topics covered, or even based on projects.

Word to the wise: Do not stress if the candidate doesn’t perform well at the very beginning. Data science is not something that can be mastered overnight. Give the candidate some time.

Conference And Seminars

There are several seminars and conferences that happen all across India every year. These events not only provide a clear picture of the data science domain but also let you understand deeply how different industries are making the best use of data science. Furthermore, networking is another advantage that these events provide to all the delegates.

Therefore, when you are in the process of upskilling an employee to make a transition to your data science department, make sure that the company allows the candidate to attend such events.  You can also take a look at the Analytics India Summit —Cypher 2019— to know about India’s largest Analytics & AI summit.

The Transition

One of the major mistakes companies make during this phase of transition is that they wait for the entire training, projects and tests to get over, and then let the candidate take that move to the data science department. However, there is a better approach to it.

After a certain point of time when both the candidate and the organisation feel that s/he has gained some significant amount of knowledge of the domain, let him/her join the data science team. Why? Because the more time s/he spends with other data scientist, the more quickly they learn.


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