Data science has become a hot topic in recent years, with businesses across all industries striving to incorporate it into their operations. One of the key elements seen in organizations successful in utilizing data science is the presence of competent data scientists. These professionals have a unique set of skills that allow them to analyze and interpret complex data sets, providing valuable insights for decision-making. However, when it comes to building a strong data science team, there is an ongoing debate on whether companies should solely rely on internal or external data scientists or opt for a mix of both approaches. As managers and CIOs, understanding the advantages and drawbacks of each option is crucial in making informed decisions for your organization’s success. In this blog post, we will discuss why having mixed teams of internal and external data scientists can give you a competitive edge over having separate divisions within your company.
With the rise of big data and data science, every company, regardless of its industry, started to want to have data scientists in its team or hire data-related services. Usually, a distinction can be made between internal and external data scientists, but it is not always taken into account when to choose one of these options according to the project and the reality of the company, its advantages and disadvantages and the possibility of working with a mixed team as an overcoming option, a difference that 7Puentes has the potential to offer.
For more than a decade we have been hearing «data is the oil of the 21st century», «behind the data there is a gold mine», «data science is the sexiest activity of the 21st century» and so many other phrases linked to all the advertising hype that exists behind big data, data science and machine learning.
Nevertheless, it is a proven truth that more and more companies are hiring the Data Scientist profile or have the desire to have Data Scientists on their team, as it is one of the most in-demand roles in the current market. In fact, one of the latest studies conducted by the social network LinkedIn states that in 2020, artificial intelligence and data science were the reasons for a 64% increase in employment on the platform, and it is estimated that this trend will continue in the coming years. According to LinkedIn, there has been a steady 650 percent increase in data science jobs since 2012. And U.S. labor market data shows that searches for data scientists are expected to increase 5% between 2019 and 2029, from 32,700 postings in 2019 to 37,700 in 2029.
Internal versus External Data Scientists
In principle, given the need to have a data scientist for our corporate project, it is important to investigate when it is appropriate to have a data scientist in the company, when it is not, and their advantages and disadvantages depending on the characteristics of the project and the organization.
Among the factors to take into account, we can mention:
- If the company wants to have a Data Science or AI department: this is a very relevant point, because it requires a lot of effort and resources to have the right talent and to train them over time, supervise them, design a career plan, etc.
- The number of projects that the company wants to undertake: this is another important factor because each project requires the strategic coordination of different areas of the company such as IT, infrastructure, services, commercial, etc. If this collaboration is not synergistic and a “data culture” is not promoted, it would not be feasible for the different projects to come to fruition.
- The commitment of the user areas: here it is key if the users are committed to the project and aligned to understand that data science in the company is a value in itself. If they do not give importance to data projects, it is not viable to move forward.
- Maturity to support data scientists: in this case, it is important whether the company has already created a data area or has prepared the ground to create it. Otherwise, it is difficult to actually hire data scientists to work within the company.
- Budget: another very important factor, since it depends on what the company is willing to invest and what resources it has to hire data science personnel or services.
Pros and Cons of External Data Scientists
Among the advantages of hiring external data scientists, who usually work in specialized consulting firms, we can highlight expert and more specific knowledge of the role of data science in companies and in different areas of the discipline (such as computer vision, natural language processing, etc.) the speed of delivery times, orientation to results and ROI (in the short-term), the project can scale (typical advantage of outsourcing) and his level of commitment is important. This is a professional who is primarily interested in completing the project quickly, with concrete results and that the client is satisfied.
As for its disadvantages, when data scientists work in consulting firms, they tend to rotate a lot, which can vary the continuity of people who actually develop the project. Companies need to have their structure and information very organized so that external people can work now. This disorder or anomie threatens precisely against a good data project. Another very important point is that the acquired knowledge does not remain within the company because there are no internal human resources, and that project management can be complex if there is no business knowledge on the part of professionals or if the company is not willing to take responsibility for the project.
Pros and Cons of Internal Data Scientists
One of the main advantages of in-house data scientists is their long-term vision, as they want to learn and train by developing a career in the company (with the exception of junior profiles or interns, who also rotate a lot). As a result, interns tend to work on long-term, exploratory or research projects that do not have the more pressured times of external data scientists; then they can work in data research labs and also (over time) train other people to create a “critical mass”, specialized knowledge that stays in the company.
Another clear point is that these internal data scientists have a greater knowledge of the business, since they work in the company and usually move in the corporate world. At the same time, an important aspect is confidentiality, because if the project is very exclusive and information cannot be shared with third parties, it is very possible that it cannot be done with external data scientists. In this case, the internal ones are a great advantage. Finally, as we mentioned before, the fact that the company has its own data science area with its own human resources is a prestige or a value that gives it a differentiation from the competition.
As for the disadvantages of internal profiles, they need to be constantly motivated and entertained (in this case, the risk is that if they get bored with their activities and projects, they will change companies), so the company needs to be very dynamic to generate interesting projects and constant challenges. Another critical point is that the selection process is longer, requires a lot of time and it is also necessary to train people and give them real benefits to keep them in the company. In this aspect, sometimes data scientists are obtained by training people who come from other fields, with upskilling, it can be a disadvantage because they are not career data scientists. There are petroleum engineers, bioengineers, or mathematicians who know a lot about machine learning who could lead the field, but they are not careerists. In short, they have to be trained, and that takes a lot of time and effort.
Comparison of both profiles for the company
We have already presented both profiles, the external data scientist and the internal data scientist. It is now necessary for the company to ask itself fundamental questions: Am I interested in creating a data science and AI department, do I have the resources and preparation to do so? Or would it be more prudent to wait until I have the necessary maturity and focus on specific results that a team of data scientists with an outsourcing modality could provide me?
Here you can see a big difference between betting on hiring and selecting human resources to work within the company with a strong long-term orientation, or looking for outsourced services, usually from a consulting firm, aimed at solving short-term projects.
The most appropriate profile depends on the company’s current situation, its institutional and material conditions, and the objectives of the project. At the same time, the type of project, the modality, the duration (short, medium, long), the business objectives and the results orientation will largely determine the type of profile to be sought.
7Puentes mixed teams as an effective and superior option
Is there a way to overcome this sharp divide between external and internal data scientists? Without a doubt, our projects and value proposition at 7Puentes show that it is and that it is truly viable. What are blended teams and what is their potential?
They are agile teams made up of a synergy of data scientists and data engineers from our consulting firm and the client’s human resources.
Working as a team with an agile data team, unique in the market, an ML engineering services outsourcing company that can meet these requirements, especially in data science projects that cannot be solved a priori by the company, will be very beneficial and easier, as we told a few years ago in this previous post.
Putting together a mix of internal and external data scientists is a big challenge, because there has to be empathy and they have to get along well. However, at 7Puentes we have more than 15 years of continuous work in data science and machine learning, more than 50 satisfied clients and more than 100 successful projects. We have worked extensively with this modality, achieving excellent alliances and partnerships with leading companies in the regional market, promoting a data-driven culture throughout the organization and an agile working methodology.
If you feel that we can help your company because the conditions for hiring internal data scientists are not yet in place, or if you want to form a mixed working team for your data project, contact our specialists today.
