Data Science and Big Data Outsourcing: How to Choose Your AI Technology Partner?

Ivan Didur - 29 June 2018 - 0 comments

Finding experts in the field of machine learning and data science is a hard and expensive task, which is why companies start considering big data outsourcing as a reasonable substitution of in-house specialists. In this article, we will examine the cases when to use data science outsourcing and the process of picking the right company.


To avoid risks clearly define on paper:

  • Type of works needed to be done.
  • Data Science skills required for the project.
  • Type of specialists you are searching for.
  • Project time range.
  • The budget for the MVP development stages.

This can be a simple Google Sheet or Airtable document. You can use this template (copy base) to compile you entire delivery timeline and MVP estimated budget in a single workspace.

Differences between freelancers outsource or companies outsource


The cheapest option is to hire a freelancer to complete the task. To narrow down the search of a right person for the job, analyze the needs and necessary technologies. Frankly, the risk of hiring a freelancer is high cause you can never be sure whether the person would finish the task or drop out in the middle of the process. The way to avoid such a scenario would be to see the portfolio and reputation beforehand making a hiring decision. If your project is big in a scale, it’s possible to hire several people and create remote development team. Freelancers can be found on the sites, such as Upwork and Freelancer.

Large Outsourcing Companies

Consider this alternative if you have a large project and you need a full-fledged and dedicated team. It’s going to cost a lot but you can be confident in the quality of the completed work. Examples of such companies are giants like Ciklum, Lohica, N-IX, AltexSoft, MasterMind, XOResearch, etc.

Small to Medium-Size Outsourcing Companies

There is also a third option – small companies providing development and design services. They have all the necessary knowledge and modern technologies that large companies have, but they are much faster and more flexible in terms of management of your project. Such companies often work with peers themselves, start-ups and a medium-sized business. And they, of course, are more reliable than freelancers, and also have greater expertise. Examples of such companies are Dataroot Labs, Indatalabs, Cleveroad.

Forming the Contract with an Outsource Company

Forming the Contract with Outsourcing Company

Several tips that come handy when forming the outsource contract:

Determine persons responsible for communication on both sides.

If it is possible, set up regular conversations to discuss the progress of the project. When the communication is established, you can solve the arising problems faster and cheaper, as well as adjusting the priority of tasks during the implementation of the project.

Dedicate 20% of timeline on the project testing.

During the work on any project, bugs and errors inevitably arise. It is a good practice to think in advance how they will be corrected. For example, take time to test the project at the end of each sprint.

Write in contract requirements for the project and expected results.

Write in contract the timeline and deadlines.

3 things to Cover When Choosing an Outsource Companies

Before signing off the contract with an outsourcing company, you need to conduct a thorough due diligence before the potential cooperation:

Check the reliability and reputation of a vendor.

Monitor the company’s case studies, check their size, and,if possible, contact the previous customers. Look for the company’s profiles on Facebook and LinkedIn. Additionally, use the search engine for finding the contextual info about the company you plan to hire.

Make sure your tasks and requirements are understood.

It would be easier for the data science outsourcing company to complete your project if you provide clear requirements and in-time communication.

Choose outsource destination.

Many enterprises choose Eastern Europe, particularly Ukraine, due to relatively low prices of solutions, high number of professionals in the field, and reputable companies out there. Also, the popular outsource destinations are India, China, and Brazil.


If using the outsourcing properly, you will receive 3 core benefits:


Outsourcing is cheaper than full-time hiring of the specialists for the team.


Outsourcing companies are interested themselves to complete your project as fast as possible so that they can take on the next one.


Hire as many people as needed and for the time period that suits you well.



If you have an idea of machine learning or data science project, feel free to contact us.