The benefits of hiring a data scientist are clear; however, not all data scientists are trained equally. The title “data scientist” can encompass a broad range of roles, including machine learning specialties, business analytics, and more.

Companies just diving into the data pool may not need a full data science team yet but rather a few specialists that can propel the business forward.

The demand for data scientists is soaring, so companies want to make sure to hire appropriately, especially as it may take awhile to find an appropriate candidate. IBM predicts the demand for data scientists will grow 28% by 2020, and the study also notes that “data science and analytics” jobs remain open for an average of 45 days, which is five days more than the market average. The results of this study showcase how difficult it can be to find a qualified data scientist and how many companies are searching for candidates.

Companies ready to hire should just make sure to hire correctly, in that they find a candidate with the right skills to help internal business needs. For those who are not sure exactly what they need, here are some guidelines to help get started.

Companies with limited data

Companies that do not collect very much data or smaller companies with limited resources should consider starting out with a data analyst. This person takes all of the data from the company and puts it to business use.

A data analyst could work with sales numbers, customer profiles, or logistics to gather appropriate data, analyse it, and present findings to the company, who can then make data-driven decisions. A data analyst could, for example, specialise in marketing to help that team make better decisions on marketing campaigns based on past performances using A/B testing and digging into Google Analytics.

Companies with large amounts of data

Businesses that have more data to analyse should hire a data engineer. This position can help ensure proper data collection methods and sound data infrastructure internally. Data engineers prepare the data, essentially making sure it’s clean and ready for analysis. After the data is ready, a data scientist will interpret and analyse the data. This person can take what was setup by the data engineer and get it ready for business use.

Additionally, companies should hire a business intelligence developer (BI developer), who can bridge the gap between the IT side and business side of an organisation. Since the data findings will eventually need to get to executives who likely have a limited understanding of data, this position helps bring the data over.

Furthermore, a BI developer works closely with the business to understand what type of analysis is necessary to help improve the internal business structure, and can then work with the data engineer on making sure that data is collected for analysis.

Companies whose product is data

Companies who have a product that is data itself need a robust data team. In addition to the positions above, this organisation would likely need a machine learning specialist or engineer. This person has more software engineering skills in order to make data function correctly in production.

Whereas a traditional data scientist looks at data to find what they want, a machine learning engineer, or a deep learning expert, makes it so the data itself can find the end result. They can build a machine or software that works and learns for itself, which is essential for companies who have a data product.

Different roles for different data needs

Of course, there are plenty of other roles in the data science world. A marketing company would likely hire a marketing data scientist, and a startup might consider employing a data creative to make innovations from big data. Many large companies at some point try to find an operational data scientist who, like the BI developer, organises a company’s efficiency by exploring processes through data.

There are also product data scientists who use data to analyse a product’s use by its users to improve the software. There are many different specialties under the role of data scientist. 

Companies who think they need a data person should begin the search now. Businesses that employ data-driven decisions are 5% more productive and 6% more profitable, as highlighted by Harvard Business Review. However, a McKinsey study found 50% of executives have more significant difficulty finding analytical talent than any other kind of role. With the role of data science in high-demand, companies that want a data person on-board should begin by identifying internal needs and the begin the search for the proper data scientist.

Further reading:

Jennifer Roubaud

Published 2 July, 2018 by Jennifer Roubaud

Jennifer Roubaud is the VP of UK and Ireland for Dataiku, the maker of the all-in-one data science software platform Dataiku Data Science Studio (DSS), a unique advanced analytics software solution that enables companies to build and deliver their own data products more efficiently.

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Lewis A, Engineer at FVBA

Businesses that employ data-driven decisions are 5% more productive and 6% more profitable.
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12 days ago

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