How can you Develop & Streamline your Data Science Team?

 As stated in Forbes, during the occasion of a roundtable webinar on “Unleash Data Science for the Model-Driven Business You Expect”, Irina Malkova, the Head of Internal Data Science at Salesforce, talks about the tasks in culminating a successful data science functionality. Irina emphasizes everything from framing business problems to getting them solved through AI tools, from collecting data to developing algorithms and further deploying strategic models and maintaining them – All a part of an efficient Data Science Team! 

No doubt, the extensive digitalization in our modern world has been influencing & shaping the global IT industry in such a way that data science stands out as the most significant technical skill of 2022. Therefore, lack of data science in the business functioning is likely to bring the greatest impediment to the company, right, on its face!


I guess you, as a business owner or leader, are certainly not up for it. If so, this blog might be of some help to you because it covers five crucial steps on how to develop & streamline your data science team. 

Now, dive into it for a complete view! 

Step 1 – Identification of Potential Risks & Remedies to the Business

Well, you cannot develop a powerpack team and streamline the workflow effectively within the organization unless and until you know what the risks and threats are to the business. You cannot unfold a remedy without critically looking at the pitfalls. 

How do you know if your upcoming business strategy is going to work? How do you know if your reconfigured data science team can dodge data fatigue? 

How do you know whether your data science team can successfully extract & retain a high revenue?

How exactly do you know whether your newly accommodated data science team can quantify their models as business innovation in the IT industry? 

Too many uncertainties, indeed! 

That is why it is primarily essential for you as the leader to identify the risks to the undertaken business strategies. Subsequently, anticipating the possible problems that may lead to creating a bad data science team or not being able to connect the team functioning to other business operations, forecasting the revenue requirements, and so forth. 

Once it stands complete, you have to formulate the remedies for such problems and pull them off! You may focus on the impact of data science in business decisions; you may try to build credibility by providing useful insights on data science to your various teams & executives, or you may seek to align career growth with the meaningful results driven from among the data science team. The choice is yours, but remember that formulation of the remedies is mandatory in the initial phase of promulgation of a successful data science team.  

Are you done? Then, let us move to the next! 

Step 2 – Search for In-House Talents

Although there is a growing need for skills like data science & machine learning, the availability in the pool is not enough! But, that does not mean there isn’t a way out. Of course, there is! While skills are mostly harnessed through experience & practice, why not search for them right within the house, within the company? 

Who knows, you may find someone already pertaining to the basic data science skills, or maybe myriads of them. Even if there aren’t any, you can always develop those skills among your in-house staff by up-taking different approaches. Sometimes, you can prong up with the approach of extensive training & continuous learning; sometimes, develop realistic data literacy across your organization; or sometimes, you may resort to striking a balance between skill development and the practices of curating data-driven decisions. 

In fact, this is your best opportunity to leverage the already-made IT resource investments.


Also, a viable option to enjoy constant scaling of the data science team!   

Wondering what’s next?

Step 3 – Tools Allocation for Individual & Team Upskilling

Yes, the third step is to allocate requisite tools & applications to the members for both their individual and team upskilling, and this is the most significant way to groom & inculcate data science skills.

Do you know why? 

It is because tool allocation shall allow your existing employees to fill up their knowledge gaps at their own time & pace, inevitably through the help of practical examples. 

The most effective data science tools are those allowing immense collaboration, such as –

  • The code hosting platform to acquire seamless collaboration without losing the project integrity, Github, 
  • Any Shareable notebook tool for interactive computing, like Jupyter,
  • Custom image dataset for fast data compilation & stocking, and
  • Dashboard tools of data visualization that are easy to use, namely, Tableau or Looker.   

Regular use of these tools shall help create a deep understanding of the critical data science operations and your team, all set to upgrade! 


Step 4 – Recruitment of Data Science Specialists

In a world where everyone is in search of expertise, your data science team shall not be complete, or in other words, solid, without hiring professionals. It means the recruitment of data scientists and/or data science specialists is a must for your company to set an edge in this rapidly transforming market environment!

Perhaps, the process of hiring data science specialists calls for a very high cost, including the data cleaning & model training operations and building front-end interfaces for the latter! 

However, a specialized & expert data science team can always supplement the other business units of your company. Well, that also suggests that you may get a professional in the team and sharpen the in-house talents to bring together a balance in data-driven decisions while up-taking it as the business decisions. 


Do not forget that there are typically two types of data science specialists, the ones who specialize in data analysis without having any programming skills. Whereas the other specialists are the ones who have an understanding of coding, statistics, algorithms, and more, and they are engaged in the program-building process! So, you need both categories in the team. 


Then comes the final task, the above-all task, that may decide the fate of your company!         

Step 5 – The undertaking of an Effective Organizational Strategy

That’s right! The final task is to create and sustain an effective organizational strategy! Here, you have to structure the data science team within your organization in such a way that the personal goals of the team match with the organizational goals.

This structuring shall, of course, vary depending on the size of the organization. So, when you manage a large organization, a centralized data science team is likely to serve better. But, if you run a medium or small-sized company, the data science team shall work better under the C-Suite executive department! 

But, as a former, you can also choose to go for a distributed data science team and the integration of data science across different business units. Or, you can also opt for a hybrid approach, somewhat like what Google & Facebook does and where data science specialists shall have specific micro-level functions.


Last but not least, for a startup, a centralized data science team with room for allocating resources in the future shall be more effective as a business strategy.     

Wrap Up: 

I hope you now have some idea about how to develop & streamline your data science team right from scratch! Even if this is not enough for you, we would be obliged to introduce you to our online corporate courses at The Open School – Corporate. These courses might be a guide to you and your employees on acquiring data science skills and making data science a part of the business functionality.  


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