A few years ago, the position of data scientist was coined as the “sexiest job in the 21st century” by more than a few news outlets – resulting in a boom of aspiring data scientists into the current market. A great way to enter this career path is to start off as an entry-level data scientist.
As so much of our world continues to be shifted online, the amount of data we now have at our disposal is almost astronomical. Estimates suggest that the total amount of data created, captured, copied, and consumed in the world is projected to reach 149 zettabytes by the year 2024. That’s more than double the amount predicted for the current year 2021.
With so much data to wade through, the demand for full-time professionals with technical skills in data managing is at an all-time high. Recruiters are racing to hire the best data analysts that can turn their messy numbers into clear strategies.
If you think you fit this job description and would like to kick-start your career in data science, keep reading. In this article, we’ll break down the job description of an entry-level data scientist. Let’s get started.
What Does an Entry Level Data Scientist Do?
Data scientists are hired by both high-performing businesses and tech startups to improve their business. Their roles can greatly vary depending on the employer.
Some organizations wrestling with big data require a skilled professional to make sense out of thousands of bytes they have collected, while others may be looking to extract key factors from their data to solve a problem.
An entry-level data scientist may be hired to explore data of customers visiting a grocery store to extract shopping trends during a specific time frame. These trends can then be used by the store to create promotions on certain products which are being bought more frequently.
What Does an Entry Level Data Scientist Do – Duties and Tasks
Oftentimes the duties and tasks taken up by an entry-level data scientist are usually dictated by the model he or she uses during their analysis or data mining procedure. Examples of these models include the CRISP-DM cycle and SEMMA.
Regardless of the process model, an entry-level data scientist may use, the starting point and key goals remain the same – understanding the problem and delivering an effective solution using the data set provided.
Below is a breakdown of the tasks a typical data scientist is expected to handle:
Understand the Problem
The problem statement is the most crucial step towards solving any data analytics problem. Usually, the person assigning the task is from a non-technical background, so it is up to the data scientist to understand what they need and design an appropriate strategy.
Sometimes the data required to solve a problem may be only partially available. In such a scenario, the data scientist would need to outline which fields must be collected before analysis can take place.
Once it is clear that all the required data is available, it is cleaned and transformed from raw, erroneous data to usable data ready for processing.
This typically takes the longest to perform.
Exploration of Data
In this stage, a data scientist seeks to uncover the underlying structure of the data that has been cleaned.
Obvious patterns are detected, along with outliers and other anomalies.
Data Processing and Analysis
Here, statistical models, machine learning techniques, and detailed algorithms are applied to the data to extract meaningful predictions and insights.
Documentation, Visualization, and Presentation of Data
Once the results of an in-depth data analysis are extracted, an entry-level data scientist is expected to present their findings in the best possible way.
Proper documentation of observations at each stage of the process is also a responsibility of the data scientist.
What Does an Entry Level Data Scientist Do – Skills and Abilities
An entry-level data scientist should have a good mix of both technical skills and soft skills to be able to fit the job description. Below are some of the main skills and abilities found in data scientists of any rank.
All entry-level data scientists should be proficient in:
- Artificial intelligence (machine learning, predictive analytics, and deep learning)
- Statistical analysis
- Command on a variety of programming languages, including but not limited to Python, SQL, R, and Java
- Knowledge of excel
- Risk analysis
- Data warehousing techniques such as ETL
- Data mining, cleaning, and preparation
- Data visualization techniques to display findings of data in the best way
In addition, proficiency in business intelligence tools is a prerequisite.
In addition to the technical skills, a data scientist should possess the following soft skills:
- Business Knowledge – understanding the requirements of a business is extremely crucial when deciding how to go about managing and analyzing the data at hand.
- Communication and Storytelling – after the data has been worked on by a data scientist, they must possess the communication skills to explain their findings and what they mean to non-technical people.
- Analytical Thinking – analyze business problems to find an appropriate solution.
- Critical Thinking – objectively evaluating the facts before arriving at a judgment or decision.
- Intellectual Curiosity – data scientists must be able to wade through thousands of bytes of data in the search for patterns about data at a much deeper level.
Furthermore, data scientists collaborating with software engineers, data analysts, and data engineers to get the best insight possible is also a key soft skill for a data scientist.
Entry Level Data Scientist Salaries
Based on a 2020 Burtch-Works study, the average annual salary for an entry-level data scientist in the US is $95,000. That’s relatively high considering the great number of professionals entering the market today.
As a data scientist climbs the ranks from junior data scientist to senior, the median salary also increases considerably. An experienced data scientist can earn up to $165,000 in an average size business.
Another factor affecting data scientist salaries is the size of the organization as well as location.
Those working in top tech companies such as Microsoft, IBM, Tableau, and Frequence can earn up to $145,000 per year. The headquarters of these companies are usually located in California or New York, so median salaries are higher in those regions as well.
How to Bag a Good Data Scientist Job
You may already be aware of how valuable the skills of a data analyst are in our current age of big data. Before you can start applying, however, there are a few prerequisites you should fulfill to improve your chances of landing a good data science job.
Get a Degree
Many recruiters favor those candidates who have formal education in data science or a related field over those who do not.
You would require at least a bachelor’s degree in Data Science or a related field such as Computer Science or Software Engineering to even get noticed.
Many candidates choose to enroll in graduate programs as well, obtaining a master’s degree in a more specialized field of data science such as data analysis, algorithms, and data visualization.
Having strong credentials can give you a great advantage in starting your data science career on the right foot.
Relevant degrees show you have both communication skills and technical skills, as well as the structure and focus on a clear goal.
Polish Your Skills
Even if your resume boasts degrees from the finest institutions of the world, it will all be in vain if your data science skills are not up to par.
This field is very practical and requires individuals with good command of a variety of problem-solving techniques.
You can practice these skills by developing your data science projects. You can gain experience by taking internships from tech startups or working with industry professionals in sectors like healthcare, banking, and education to solve problems.
This will also give a good impression to your recruiter as it will show your passion and commitment to the field- bringing you one step closer to becoming a good entry-level data scientist.
Having strong contacts in your desired field is always an advantage when looking for employment.
Try to seek out a senior data scientist that can offer you a different perspective based on their expertise. This can be insight based on their many years of experience, knowledge gained from a Ph.D., or an advanced skill set.
If you are unsure of where to look for contacts, LinkedIn is always a great place to start. By seeking out the advice of a full-time data science professional, you can help to plan your career path in the most efficient way.
Other Common Data Science Job Titles
Every organization has a different goal they want to achieve with their data. This means that every task or data science problem requires a different area of expertise.
The term “data scientist” itself is quite broad and can include individuals from the various disciplines of data science.
Let's discuss some of the most common job titles in the field of data science before
This is the most general data science role, covering a little bit of everything. A data scientist knows all aspects of a project and can use their expertise to develop new algorithms and uncovering trends in data.
A data engineer makes sure the data is ready to be used by data analysts and data scientists for processing. They are responsible for cleaning, designing, and organizing data collected from different sources.
A data analyst usually has the most business knowledge of other data science roles. They work on manipulating large datasets to gain insight into solving the business problem.
Some data analysts are in charge of data visualization and must be able to present their findings to the business side of things.
You may be interested in entering the field of data science. Before you hand in your resume, however, it is important to know the details of what exactly an entry-level data scientist does all day and whether or not you’re cut out for the job.
Every organization has database systems that need to be designed, created, and maintained. This is done by a data architect, who has both technical and business knowledge to get the job done.
Machine Learning Engineer
Once a data scientist has set up a model, a machine learning engineer feeds it with data.
They also build programs using artificial intelligence techniques to enable a machine to think for itself.
Business Intelligence Specialist
A business intelligence specialist transforms unstructured data into data warehouses using the ETL model.
They are needed to design technical solutions to the business problems of a company.
Becoming an entry-level data scientist offers plenty of perks. The work is engaging, in demand, pays well, and offers substantial growth for a good career path.
Of course, these benefits require data scientists to have expertise in a long list of both technical and business skills. No data set or business problem is the same, so data scientists need to be prepared to tackle each problem separately- while also keeping in mind exactly what it is that the organization wants to achieve.
Entry-level data scientists are usually part of a bigger team and answer to a mid-level or senior data scientist while working. Because of this, entry-level data scientists need to be proficient in their communication skills, teamwork, and multi-tasking abilities.
Every year thousands of aspiring data scientists, data analysts, and data engineers enter into the market to obtain entry-level jobs- yet the job postings asking for such work seem to only increase.
Taking the time to learn the skills required for succeeding in a data science job can improve your chances of landing a position at a good organization. In the end, becoming a great entry-level data scientist is all about the insight you bring to the table when solving a problem.