Entry-Level Data Scientist Job Description: Roles and Duties

Due to the importance of the ‘new gold’, i.e. our data, the data scientist is becoming an extremely important and revered individual within the software development community.

With product success being such a fundamental aspect of a business’s ability to achieve its wider goals, businesses have now turned to the power of data to help them understand their users better and to help improve their business services and processes.

In this post, the topic of discussion will be centered on entry-level data scientists. We will look at the role they play within organizations, the responsibilities they carry out as well as some key insight all businesses should bear in mind when hiring them.

Understanding The Purpose of Data Science

Before jumping into all the details regarding the role and responsibilities that entry-level data scientists fulfill, we must first begin by laying out the fundamentals of the field in which they work; namely data science.

Data science is a term that encompasses the many fields of data-driven disciplines. These include:

• Computational statistics
• Data mining
• Machine learning
• Artificial intelligence

Conversely, one can view data science as the intersection where computer programming crosses paths with mathematics and social sciences. By its very nature as a multi-disciplinary field. It is typically associated with the worlds of business and finance.

Data science is the art of extracting meaning and insights from data. Using the information extracted, companies find themselves in a better position to make more informed decisions that have a positive impact on their ability to achieve their objectives.

Data science levies mathematical concepts such as algorithms, statistical models, and machine learning tools, all of which are applied to large sets of data in order to discover patterns, tendencies, and even anomalies within data. Based on these factors, insights are produced that can be used to understand and even predict customer behavior, etc.

Data Science vs Data Analytics

Prior to delving into the role and responsibility of an entry-level data scientist, it is crucial to differentiate the field in which they work from another similar-albeit-distinct area called ‘data analytics’.

This is extremely important as some organizations believe that a data scientist has the same role as that of a data analyst and vice-versa. However, while they are alike, they are not identical.

Data analytics is a field that involves the use of statistical techniques to uncover patterns or trends in data. Data science, on the other hand, relies heavily on statistics and machine learning tools for them to extract valuable insights from large sets of raw data.

A data analyst could be considered as a statistician who may also conduct some basic analysis. However, a data scientist uses statistics and analysis as just one of their many tools to extract valuable insights from large sets of raw data.

To make this even more tangible, a data analyst needs to have an understanding of how to navigate computer programs whereas a data science professional would need more knowledge about machine learning algorithms and how they can be leveraged to extract data.

In short, the data scientist needs to have more competence in technology and machine learning algorithms for data extraction than a data analyst.

The Role of an Entry-Level Data Scientist

Having set the parameters which define the field of data science, let us now focus on the role played by an entry-level data scientist in an organization.

As mentioned previously, data science is a multidisciplinary field. Therefore, the role that an entry-level professional fulfills in the field will vary based on the organization and its specific needs.

That said, generally speaking, an entry-level data scientist is expected to leverage machine learning and AI algorithms to extract insight that may be relevant to an organization. They apply these algorithm-based technologies on pools of raw data that can be both structured or unstructured (i.e. Excel profit figures vs. facial recognition data)

The distinguishing factor between the role of an entry-level data scientist roles and that of a senior professional is based chiefly on the level of responsibility expected from each one.

For example, in a data science department, a senior-level data scientist will be expected to oversee the work done by their entry-level counterparts, thus assuming a more managerial role where they sign off on all activities or tasks done.

An entry-level data scientist will not have that sort of responsibility. As opposed to being a managerial figure, their role will be more centered around compiling data models and extracting them to retrieve insights.

The Responsibilities of an Entry-Level Data Scientist

The types of activities and tasks that an entry-level data scientist is expected to carry out will depend on the type of organization in which they work.

That said, across most businesses, entry-level data scientists share the following responsibilities:

● Collecting and analyzing data: This is the primary responsibility of entry-level data scientists. They are tasked with collating the relevant information from different sources, including databases, surveys, social media posts, or customer feedback forms. They then compile this data into a useful format for analysis. They also use analytical software to identify patterns in the raw numbers.

● Presenting findings and recommendations: After analyzing the data, entry-level data scientists need to present their insights clearly. This includes explaining quantitative findings as well as drawing conclusions based on qualitative information. They will also recommend future actions based on the collected data that can be taken for maximum impact. To do so, they usually create dashboard reports for executives that summarize their thoughts on the analyzed data.

● Researching and testing new tools: This includes exploring a wide range of analytical software to see what best suits the company’s needs. Entry-level data scientists will also test out new machine learning algorithms, predictive models, and other technologies to gauge their effectiveness.

● Running predictive models: Entry-level data scientists will have to run analysis and predictive modeling in order to identify, explore, and recommend solutions. This includes examining previous data samples in order to determine data patterns which will then help to inform what may happen in the future.

The Salary of an Entry-Level Data Scientist

Having explained the type of role that entry-level data scientists fulfill as well as their respective responsibilities, let us now focus on the financial compensation offered to them.

According to data shared by PayScale, the average salary of an entry-level data scientist in the United States stands at roughly $96,000.

This statistic is further supported by GlassDoor which estimated the average salary range to be around $92,000.

Needless to say, there are several elements that affect the salary that an entry-level data scientist gets. These include job security, numbers of hours worked weekly, location, as well as the potential for future growth in the organization.

To provide an example of how differences in salary can pan out in a real-world context, an entry-level professional in data science who resides in the San Francisco Bay Area can expect to earn a higher salary than one who is based in Washington, DC.

This is because San Francisco is considered more of a tech hub than Washington thanks to Silicon Valley. It also implies that data scientists expect greater and faster career mobility than those who are based elsewhere.

When Should Businesses Pay More for an Entry-Level Data Scientist

Ideally, when bringing onboard data scientists for an entry-level position, the wisest thing is to invest more to secure a top candidate. Needless to say, this will involve aiming at the higher end of the spectrum.

This is the most logical option for businesses, given that it will make a significant difference in how well they can leverage data-driven initiatives to achieve their goals.

Moreover, when investing more in an entry-level data scientist’s salary, there are other benefits as well:

• Higher retention rates
• Better value of output
• An overall better quality candidate
• Better professional relationship

The best way forward would be to invest heavily at the beginning so as not to regret it later on. This means going beyond what is typically offered and making sure you have budgeted enough funds for this particular need.

The Difficulties of Hiring via Freelance Platforms & Job Boards

Hiring an entry-level data scientist via a freelance platform or job board is an extremely dangerous practice.

For starters, there is no guarantee that the person you’re hiring will be able to do what they say they can. This is because these platforms do not implement any sort of vetting process that evaluates, assesses, or validates the legitimacy of their service provides.

Chancers also take advantage of this to falsify documentation, qualifications, and experience to lure unsuspecting businesses into hiring them. By the time businesses realize that they are not legitimate, time, money, and resources have all been wasted.

Recruiters who choose to assume the challenge of vetting candidates from a freelance platform assume the risk of letting illegitimate professionals slip through the cracks. With data science being such a crucial aspect of modern-day business success, all risks in the recruitment process should be avoided.

What’s more, while there are some candidates who have the technical prowess to assume some of the responsibilities of an entry-level data scientist, they may not have the right soft skills or project management expertise to ensure the success of your business operations.

Where to Hire Entry-Level Data Analysts

Instead of assuming the risk of posting a job advertisement via a job board or freelance platform, the best way to begin your search for talented data analysts is via a vetted online software community.

These types of platforms connect businesses with their full-time software professionals who can assist them with their business operations.

They stand out from freelance platforms and job boards in the sense that they assess, evaluate, and validate the skills, experience, and expertise of any member who wishes to form part of their community.

An example of a top vetted community is DevTeam.Space. They are a community composed of legitimate professionals within the software industry who partner with businesses all over the world to help them achieve their goals. Since all of their members have been validated, businesses are guaranteed that the professionals that they partner with maintain high standards of quality.

Through DevTeam.Space, you can partner with real data scientists who have both the technical capabilities and project management support to have a positive impact on your business operations.

Secure Talented Entry-Level Data Analysts Today

When hunting for this type of software professional, stay clear of job boards and freelance platforms, even if you are hiring for an entry-level position.

The risks far outweigh the benefits.

DevTeam.Space is a vetted community that can help you get the talent you need.

Discover how their process works by taking a minute to complete their questionnaire.

Once you do, one of their account managers will reach out to you as soon as possible to provide you with the information you need.