Access to data research gives business people the opportunity to improve performance,  review sales strategy, and engage new clients. And that trend will only increase as cutting edge technologies like AI, ML, and predictive modeling add new power to analytics—and redefine business profit.

Digital newbies believe that data research requires fully automated algorithms. Startups talk about a serverless architecture, you can hear  about Software as a Service Software (SaaS), continuous integration, NoOps. Many believe that it will be enough to involve a skilled Data Scientist for once only to set up a smart data model, where the system will cast magic on its own and for years on end.

The reality is different. If you visit the Tesla factory, you will be surprised with the amount of manual tasks. The staff is so busy with routine operations that Tesla is very much akin to a Chinese mobile factory.

On the other hand, Elon Musk offers another model of the future, and we want to trust the oracle.

manual tasks in data research interview at data researcher position

Typical data research use cases

Let's review typical tasks for data researcher:

  1. An IT company develops an enterprise Product, analyzes the market, and estimates engagement of 2-3K potential clients with more than 1000 employees. How to sort them and perform smart outreach decision-makers?
  2. Smart tools analyzing corporate website traffic and provide an in-depth report about your visitor's. How to get in touch directly with potential clients?
  3. You analyze a competitor's forum and have a list of nicknames of your potential clients. Next steps?
  4. You download LinkedIn contacts and want to extend profile information with proper mails and networks like Twitter, Facebook, Instagram or even Tinder
  5. Machine learning service used to pre-select the best candidates from a huge audience of potentials for Product alpha testing. But the selection algorithm is too rough. How will make clearance of data to improve the algorithm?

Data Research full automation gap

You can observe a similar situation in the field of automatic data processing. Any automatic algorithms can guarantee effective results.

  • To obtain excellent results, effective answers, or provide data visualization for decision makers, you have to clear data and filter it.
  • To identify correct patterns you have to review all of them.
  • To identify the best logistic track, you have to review thousands of trips.

Twenty years ago people thought that the computer age will reduce paper usage in the offices. Today, we can see that the human civilization produces more and more garbage. The same can be applied to the digital age - our environment produces a tremendous amount of data: public and private; structured and non-structured; for humans and services; pictures and text; tags and bits; online and offline.

In a computer system consisting of people and machines, the element with the greatest flexibility will be the controller  (c) Andrew Shark

Finally, only a complex system that uses the best algorithms, the smartest scientists and accurate human correction can become a competitive weapon in the global market.

What is the difference between a data researcher and a data entry specialist?

The data entry role usually requires a junior specialist or a student doing manual input of data. In most cases, data entry works with the input forms of databases, sheets.

Data entry means copying information from paper documents, unstructured sources, audio subtitles, logs, systems, and etc. The main skills for this position are keyboard speed, ability to tolerate the routine job, and being attentive to details.

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A data researcher can do all of this as well but also:

  • Finding new information, buying request or template
  • Knowing a lot of actual sources of information and being able to find new ones
  • Improving own results and generating new information based on rough data

What key specialization a data researcher should have?

Data Researcher can be a universal soldier, but it's better to develop superhero profile:

Data researcher for direct mail lead generation

Researcher of contacts and potential Clients. This is the most common need for research. For example, an expert in LinkedIn research, social networks, grabber tool, catalogs and databases.

Can generate email better than any tools except Gmail.

Read a related article about Researcher of Contacts and Clients and use it for direct email lead generation.

Data Researcher for data clearance

Data researcher supporting a Data Science team. Usually skilled in Mathematics, basic data algorithms, tools like Excel, databases, scripting, and knows the main models like Gauss and Mathematical statistics.

Can make a linear forecast or say the value for the third quartile.

Data Researcher for Data Sources

Data researcher with a strong passion to increase data entropy. Can download or migrate data from websites, public databases, torrents, and unstructured media. Knows numerous grabbing tools Python, Excel, and web parsing.

Can download the whole Internet if provided a proper storage.

What are  key questions to hire a data researcher?

  1. Explain your domain area. Which information are you required?
  2. Add more detail about your actual data. Is the data structured? How do you store it?
  3. What information do you think you require?
  4. Do you know how to use this information in the best way?
  5. Explain special requirements.

Data research interiew questions

Why data researcher from Datarob is best choice?

We really do this job with an excellent level and unintended it. Datarob has a strong focus to provide:

  1. Low attrition.
  2. Smart staff.
  3. Help with SEO, an Cloud infrastructure like AWS, scripting, databases or API integrations.
  4. Deep integration with the Data Science team.
  5. Deep screening. Only the best of the best staff.
  6. All data researchers have Upper-Intermediate or higher English proficiency. Some also speak French or Spanish.
  7. Direct contact. We trust clients and researchers, so you can work directly with them via any messengers, phone, and mail.
  8. High availability model. We cover vacations and sick leaves.
  9. Happy staff. Zero overtime.

Data researcher salary

You can select the most comfortable cooperation model. If you don't know, we will suggest the options to hire data researchers. Available models are:

Dedicated team starting from one senior data researcher

At Datarob, we only work with senior Data Research staff. Our minimum requirements are:

  1. 3-5 year of experience with data.
  2. University degree. Yes, it's possible for Ukraine.
  3. Pass 1-10 interviews with senior Data Researcher staff.

We can guarantee extra high quality of data for leads generation, data analytics or data visualization.  The average rate is $2K per month for senior staff.

Project-based

Data researchers can be involved on a project basis: Starting from $5000 for an independent project, only with data research.


Start from $0 for any complex solution for a data scientist, data analytics or visualization.

data researcher project managment

Outstaffing model with Data Researchers or Data Entry

Outstaffing model with Data Researchers or Data Entry

We hire best professionals for you and provide a full support package (local management, office, taxes, insurance, and financial support).

This model starts from 5 full-time employees with a flexible monthly fee.

Next steps to hire Data Researcher

Please send us request for Data Research to our team  https://datarob.com/contact/. We will be happy open your mail with:

  1. Your company introduction including corporate website
  2. Short Project / domain description
  3. (optionally) Expected data sources and storages.
  4. (optionally) Your pain and how we can help you

Connect us hello@datarob.com

hire data researcher datarob