How big should a company be to use data science

In data science, there are many possibilities that make it confusing when picking a path. Does the size of a company matter, or how big should a company be to employ data specialists? As a professional, you need to work with an organization capable of rewarding you and, depending on your ambitions, somewhere you can grow. Data scientist jobs are different, and the company’s size does matter. 

If you are hired in a startup, it can be very different from being hired in more prominent companies. Picking the right company can be complicated. 

1. Small companies and startups building data

In most cases, such organizations are many with a size of below 150 people. They draw in scientists, promising them a better scope and a chance for creative expression of tackling issues. In such a working environment, be ready to be busy. When you join a company in the early stages, much must be done. You may find yourself juggling different data roles at once. You may be responsible for sourcing, cleaning data, and making frameworks for others. 

These companies have a high hiring bar. Often, a company may only need data scientists at a later stage. However, if such a company feels like they need one, credentials are very important, which raises the bar. You need to work independently and with little direction in such an organization. You will have to deal with veterans in the industry seeking challenges but with little expertise in dealing with startups. You need to handle great workloads, and you may get paid much less. 

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2. Small companies and startups handling their data

These are companies with less than 300 people. There is a market penetration that a company must reach before being ready to hire dedicated specialists to analyze data and make decisions. The jobs are not typical in smaller companies but are more prevalent in companies building data tools. There is a high hiring bar in this case; founders may be looking for hybrid skillsets like data scientists with excellent skills in visualization, statistician, and data architect. In this case, a degree may be required. Such companies look for soft skills as well. Communication is essential for one to be hired in this kind of company. Creativity and solving problems are essential. In this kind of company, a data scientist is responsible for picking the tools that will be used. 

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3. B2B vs B2C

For B2B, the companies don’t handle large volumes of data. Special attention is paid to data characteristics and details. B2C companies have massive data amounts, mainly about their customers. Problems are mainly about the large data sets and dealing with them. For more prominent startups with 100-300 people, one must differentiate between B2B and B2C companies. The problems handled in the companies are not the same, mainly because of the amount of data in question. It is essential to understand a company’s data issues and business model first. Some companies may not be able to support growth. 

For B2B companies, products are sold to enterprises. There isn’t much data to handle here in terms of volume. Special attention is paid to the details of the data and its characteristics. There is a need to focus on depth. Each company dealt with brings in large amounts of revenue. B2C companies deal with individuals. The companies deal with many people at a time, translating to many data issues because of scale and handling large data sets. In such companies, scientists need to focus on the width of data.