Do you really need to hire a Data Scientist?



Data analytics has been gaining hype lately, and many organisations seem to be hiring a Data Scientist to tackle their data-related challenges. You might have started wondering if you should follow suit, but what does a Data Scientist really do? There is no prescribed formula to follow for adopting data analytics and hiring of a Data Scientist. So where do you even begin?

Quick Links
Understanding the different roles in a Data Science team 
Considerations for Hiring a Data Scientist    
Objectives of companies at different scales
Centralised VS Decentralised reporting structures

A Data Scientist is an expert who uses tools to collect, analyse and derive insights from large data sets for the purpose of helping organisations better their operations, overall performance and gain a competitive edge over rivals.

As the responsibilities imply, a Data Scientist can be widely leveraged on by different departments in an organisation, such as marketing, business operations, production and personnel management. They offer important inputs in the planning process, helping to identify useful insights and derive statistical reports for planning, executing, and monitoring results-driven business strategies. Whether that is marketing analytics, or manufacturing analytics, predictive sales analytics or demand forecasting, data plays a very important role in any business, and Data Scientists are your catalyst to insight.

However, hiring a Data Scientist may not be a financially viable option for those that are bootstrapping. Your organisation may have specific needs that do not require a Data Scientist to fulfil.

In this article, we break down the different considerations so that you can make an informed decision based on your needs.

Understanding the Different Roles in a Data Science Team

Before you decide on the best solution for your organisation, seek first to understand the different roles and functions within a Data Science team since their roles are vastly different.

Data Analyst

Data Analysts can interpret pattern and trends within data sets. They also actively seek new data collection methods and analysis processes. They then translate these trends into understandable insights that can influence decision-making in an organisation.

Data Engineer

Data Engineers design a company’s data architecture, taking into consideration how the data will be gathered, stored, retrieved and distributed. They use specialised software to maintain a secure database environment, including defining data recovery processes.

Data Scientist

Data Scientists interpret data from patterns and trends, and provide strategic insight that help decision-makers make optimal, data-backed decisions. More specialised than Data Analysts, Data Scientists have deep mathematical and coding expertise, enabling them to build algorithms and predictive machine learning models to answer specific business questions.   

Project Manager

Project Managers in Data Science teams possess technical and analytical skills. They define the cost, resources, time, scope, deliverables and risks of the project. Project Managers are also responsible for ensuring that the project is executed effectively from start to end. They make proactive decisions to solve discrepancies among stakeholders, while ensuring that the project’s constraints are balanced.

Considerations for Hiring a Data Scientist

Even with the different Data Science roles demystified, you may wonder if hiring a Data Scientist is a worthwhile investment for your company.

Let’s be honest, not every company needs to hire a Data Scientist. The scope of a company’s projects is a good indicator of whether hiring a holistic, full-time Data Scientist is necessary. Does your organisation have enough data to warrant the hiring of a Data Scientist? For many small and young enterprises, the answer is a resounding ‘No’. But, of course, this depends on the business problems that require solving, as well as the amount of information your business works with. 

Another question you might ask yourself is “Do I need a different expert?” For example, if your firm requires analysis and insights, a Data Analyst may be more suited for the role. Unless the scope of your business problem is substantial and requires predictive models to be built, a Data Scientist is a luxury only if you have the available resources.

If your company faces the following situations, a Data Scientist may be required:

  • The need to process vast repositories of data
  • Multiple business problems that require deep technical skills and expert intervention to be solved
  • Long-term projects that require commitment

A Data Science veteran who possesses business acumen and deep technical skills can be the optimal solution for your company in such cases. They are likely to see your long-term business projects through completion, helping you navigate your resources towards solving your pain points in the process.

objectives of Companies at Different Scales

Here, we illustrate 3 scenarios to help you evaluate the scope of your projects.

Companies with limited data: Small-scale objectives

Smaller companies with limited resources or companies that do not collect large data sets should consider starting with a Data Analyst.

For instance, a company that aims to increase sales within a specified time period may leverage on a Data Analyst. The Data Analyst can highlight key customer profiles and buying patterns, which will enable decision-makers to change their sales tactics to suit their customers better.

Companies with large amounts of data: Mid-scale objectives

Businesses that have sizable data sets to be analysed will likely need to hire a Data Engineer together with a Data Analyst or a Data Scientist. In such cases, the Data Engineer sets the data infrastructure in place, so that the Data Analyst can derive preliminary insights from the data sets. The Data Scientist will then delve deeper into the insights, providing strategic directions and building predictive models to solve business problems.

Once the data collection methods are defined and infrastructure is ready, a Data Scientist will then interpret and analyse relevant data points.

For instance, a company that aims to reduce manufacturing bottlenecks and enhance real-time communication with partner companies may rely on a Data Engineer and a Data Scientist to solve such daunting and resource-heavy pain points. 

Companies whose product is data-driven: Large-scale objectives

Companies whose products are innately data-driven will need an active Data Science team. In addition to the positions suggested, such companies will likely need a Machine Learning/Deep Learning Expert. Machine Learning Engineers are sophisticated programmers who build machines and systems that can learn and apply learned knowledge to make future decisions.

A traditional Data Scientist looks at data to find what they want. A Machine Learning Engineer, or a Deep Learning Expert, engineers the data so that the data itself can find the desired results. They build machines or software that works and learns for the enhanced efficiency.

Centralised VS Decentralised reporting Structures

Under a decentralised reporting structure, Data Scientists report directly into different functions or business units across an organisation. This reflects the utility of data science as a function within business. Of course, such a reporting structure would require the scale of a large business, that have distinct business functions.

The drawback of such a structure is the restricted mobility of data scientists, which can lead to knowledge silos and fewer career and learning opportunities. Decentralised reporting can also make it more challenging to implement quality standards, shared infrastructure, and inhibit standardised practices.

Conversely, centralised reporting structures have Data Scientists reporting to Chief Data Officers. This is much more prevalent among small to medium enterprises as it is a more efficient implementation of headcount and resourcing allocation. In such a structure, Data Scientists tend to have more collaboration and mentoring opportunities than their peers on a broad range of projects, thereby providing better career growth. The drawback of course, is that Data Scientists can be overloaded with disparate projects, stretching them beyond their capacity to the point where they are neither learning nor growing in their roles.


The benefits of data science are apparent, but with the increasingly high demand for Data Scientists across all industries, small to medium companies will be stretched to obtain suitably qualified candidates.

A company’s “data capabilities” can include a broad range of roles, ranging from Data Engineer, to Data Scientist to Machine Learning specialties, and many more.

Not all companies require the full gamut of specialists and resources to achieve these data goals. Those that want a data capability resource should begin by identifying business goals and objectives before deciding on a hire. Another consideration should be given to outsourcing third party vendors who specialise in data analytics as a service. Specialised Vendors are committed to providing data analytics services to small to medium enterprises that may not otherwise have the resources to implement internally. These vendors will almost always have more time and resources to dedicate to data analytics than any non-data focused business will.

DataVLT is providing data analytics as a service to companies. Find out more about our complimentary assessment here.




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