Outsourcing and outstaffing - what are the differences?
- Ariel K
- Sep 2, 2023
- 3 min read
Outsourcing and outstaffing are two different approaches to staffing and resource management. Here's a breakdown of outsourcing vs. outstaffing :
Outsourcing:
- In outsourcing, a company contracts with an external service provider to handle specific tasks or functions.
- The external service provider is responsible for managing and executing the outsourced tasks.
- The company retains control over the overall strategy and decision-making process.
- Outsourcing is often used to reduce costs, access specialized expertise, and improve efficiency.
Outstaffing:
- In outstaffing, a company hires employees through a third-party agency and assigns them to work on specific projects or tasks.
- The company retains control over the employees' work and manages their day-to-day activities.
- The third-party agency takes care of administrative tasks such as payroll, benefits, and legal compliance.
- Outstaffing is often used to quickly scale up or down a team, access specific skills, or manage temporary projects.
In summary, outsourcing involves delegating tasks to an external service provider, while outstaffing involves hiring employees through a third-party agency. The choice between outsourcing and outstaffing depends on the specific needs and goals of the company.
Outstaffing remote data science teams
Many companies prefer outstaffing over outsourcing when it comes to data science and the reason is simple - unlike regular software development work, data science is directly connected to the core asset of the company - it's data. There, it is crucial for the company to retain control and know exactly what is being done with its data. This is only possible when the company retains control over the employees' work and manages their day-to-day activities - as is customary in outstaffing.
This has become increasingly popular in recent years. Many companies are realizing the benefits of accessing highly skilled data scientists without the need for extensive recruitment processes or long-term commitments.
When a company decides to outstaff a remote data science team, they can tap into a pool of talented professionals who are already vetted and experienced in their field. This saves the company valuable time and resources that would have been spent on searching for and hiring individual data scientists.
Additionally, outstaffing allows companies to quickly scale up or down their data science capabilities based on their current needs. If a project requires additional expertise or manpower, the company can easily request more team members from the outstaffing agency. On the other hand, if the project is completed or the need for data science diminishes, the company can reduce the team size without any long-term commitments or financial burdens.
One of the key advantages of outstaffing remote data science teams is the ability to access specific skills and expertise. Data science is a rapidly evolving field, and it can be challenging for companies to keep up with the latest technologies and techniques. By outstaffing, companies can ensure that they have access to professionals who are up-to-date with the latest trends and can provide valuable insights and solutions.
Furthermore, outstaffing remote data science teams can also be a cost-effective solution for companies. Instead of hiring full-time employees and providing them with benefits and office space, companies can simply pay for the services provided by the outstaffing agency. This allows for greater flexibility in budgeting and reduces the financial risks associated with long-term commitments.
In conclusion, outstaffing remote data science teams offers companies a flexible and efficient way to access highly skilled professionals, scale their capabilities, and manage their data science projects effectively. With the right outstaffing agency, companies can focus on their core business while leveraging the expertise of remote data science teams to drive innovation and success.





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