There is not THE Data Scientist#
If you work with ‘data people’, expectations often are that they have access to all data and can answer all questions of everyone in a company. They are often referred to as the ‘data guys’ and if you don’t know their exact title, your first guess is likely to be ‘Data Scientist’, even though their duties, capabilities and stakeholders might differ blatantly. ‘Data Professionals’ is the better high-level term that includes all roles from Data Scientist to Data Analyst and Data Engineers and all flavours in between. If you don’t work with Data Professionals on a daily basis, chances are high that you have a hard time to distinguish between different roles.
You will most likely earn a ‘facepalm’ when mixing those roles up in front of Data Professionals. In this article I will give you a quick overview of different roles in the broad field of Data Science.
Roles of Data Professionals and the skill set needed#
An overview created by PwC shows five different roles of people working with data with the major skills they need to possess.
Before diving into the different roles, let’s quickly describe the most important skills shown in the matrix:
- Domain knowledge: This refers to the business or research field that a company is operating in. Not everyone in a bank needs to be a finance expert to do their job.
- Visualisation: This skill is all about being able to present insights from data to a specific audience. A PhD in Mathematics might be great at statistics, but not necessarily the best person to present the outcome to a non-technical audience.
- Data governance: This also includes ethics and security. Due to GDPR we now have clearer guidelines on how data can be used. A central aspect is to avoid the use of personable identifiable information (PII).
- Engineering: Sometimes solutions created by Data Professionals need to be integrated into existing products or are planned to be standalone products used by a wide audience. Engineering skills are thus of tremendous importance in some cases.
- Management/Curation: This includes Data Sourcing, Data Cleaning and Data Manipulation with the main goal of preparing data for further efficient processing.
- Analytical approaches: This refers to the complexity of the analytical methods used to solve a given task. Most of the time reports for a sales teams are purely explorative, while Data Professionals working on the product might need to solve e.g. routing problems based on sophisticated statistical models.
- Machine Learning: Machine learning is about teaching computers to recognize patterns without being explicitly programmed.
Let’s dive into the 5 different roles#
1. Decision Makers: Decision makers are typical users of insights and models created by Data Professionals. Their main task is to be able to interpret results correctly and understand the impact on the business. They should have a high-level understanding of what a Data Professional did and be able to read and interpret their work and visualisations. Decision makers who went through a Data Professional career themselves are often better equipped to understand a conclusion of their team and make correct strategic decisions based on it.
2. Functional Analysts: Functional Analysts are domain experts and are usually supporting one specific department. Typical examples of Functional Analysts are Product Analysts, Marketing Analysts and Sales Analysts. Their main job is to find answers for a set of business-related questions and ‘translate’ them to their colleagues so that they receive quick and clear action recommendations they can follow. They are also in the group of analytics-enabled jobs, meaning that they often access a functioning data infrastructure created and maintained by other Data Professionals.
3. Data Analysts: Data Analysts are often more versatile and tech-savvy than Functional Analysts but can lack domain knowledge. They tend to work with many distinct stakeholders. When creating a QBR for clients e.g. Functional Analysts are usually better to understand the content that is presented to a client. However, Data Analysts are often better in preparing the data for a specific use case and aim to automate and scale solutions. Another job title often used by companies is ‘Business Analyst’, which sits between a Data Analyst and Functional Analyst. Business Analysts often cover more than one specific field or stakeholder within the company but tend to use existing data infrastructure and be less tech-savvy than Data Analysts.
4. Data Engineers: Data Engineers are – as the name already tells – the engineers among Data Professionals. They are excellent in integrating the work of Data Professionals into existing technical infrastructure. They are also the ones building the data pipeline, so that everyone else has the data they need available in a clean state. On the other hand, they are usually part of a data team or engineering team and have other Data Professionals as their stakeholder, meaning that they are not the ones answering and presenting results of business-specific questions. Depending on the exact task and team organisation, Data Engineers can often be found in pure Software Engineering teams as the tasks and style of working are closer to Software Engineers than to Data Professionals.
5. Data Scientists: Data Scientists are combining all the mentioned skills. They need to be proficient in statistics, domain knowledge, coding, engineering, and communication. Due to the unusual mix of complex skills, they are the ones working on high-impact projects within a company and have a deep understanding of what other Data Professionals and businesspeople are doing. Be aware that this kind of skill set is rare, and Data Scientists are most efficient when teamed up with other Data Professionals that can help to make a Data Science project successful. Also, a Data Science role is not an entry level position - they have usually worked as a Data Analyst (or Data Engineer) prior to becoming a Data Scientist.
Keep in mind that this is a high-level overview. To learn more about the topic I recommend resources such as Data Analyst vs. Data Scientist or Data Engineer vs. Data Scientist before exploring the details of each role individually.
Data Professionals are here to stay… and hard to evaluate#
The distinction of roles in the field of Data Science is an important one. Why? Because this field of work is just getting started. The jobs described above are likely going to go through a similar development as Software Engineering in the 90s. Whether you are working as a Manager or individual contributor in a specific department such as Marketing or HR, being able to understand Data Professionals is key to collaborate with them and to benefit from their work.
Skillfill built a technology to assess applicants and your own data team to help you building the best team possible via hiring and learning recommendations. Book your demo to find out more