It takes only a single Google search to find multiple data scientist job descriptions. Many employers are looking for qualified professionals in the field - no matter your geographical residence, you can almost always find a company that requires a data scientist. However, before you go ahead and search for a job position that’s appropriate for you, you need to know all about the data scientist qualifications. And that’s exactly what we’ll talk about in this tutorial.
- 1. Responsibilities of a Data Scientist
- 2. Different Groups of Data Science Qualifications
- 3. Entry-Level Data Scientist
- 3.1. Responsibilities
- 3.2. Requirements
- 3.3. Career Path Options
- 3.4. Salary
- 4. Junior Data Scientist
- 4.1. Requirements
- 4.2. Responsibilities
- 4.3. Career Path Options
- 4.4. Salary
- 5. Senior Data Scientists
- 5.1. Requirements
- 5.2. Responsibilities
- 5.3. Career Path Options
- 5.4. Salary
- 6. Are You Ready For a Data Scientist Path?
Responsibilities of a Data Scientist
Data scientist job description usually asks for a person who is able to support the product, leadership, and marketing campaigns with insights from analyzing the data. The person behind the data scientist position is usually expected to be able to work with large sets of data to find ways to increase the effectiveness of companies' actions.
The majority of employers expect data scientists to have a significant amount of data mining and its analysis methods. In addition to that, they are expected to have a good grasp of data tools, also how to build and implement models, use or create algorithms and simulations.
Some of the key responsibilities for data scientists are:
- Mining and analyzing data from companies' databases to make conclusions on how to make optimizations in companies' product development and business strategies.
- Develop custom data algorithms.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop tools to monitor and analyze performance.
- Looking for new tools to increase the effectiveness and accuracy of data sources.
- To organize and use predictive modeling to make better UX, revenue generation, targeting, and business outcomes.
To be able to work with these responsibilities, most of the data scientist job descriptions will ask for these qualifications:
- Good and quick problem-solving skills while developing a product.
- Experience of data architecture creation.
- Knowledge of machine learning techniques and what are the drawbacks or advantages of it.
- Knowledge of computer programming languages that help to draw insights from the data.
- Most of the times a university or college education in data science is needed but nowadays it can also be achieved through learning paths online.
- Excellent communication while working on the same goal in a team.
- Knowledge and experience of a big variety of internet tools like Google Analytics, DigitalOcean, Facebook Insights, Redshift, etc.
Different Groups of Data Science Qualifications
There are a few different types of data scientist job descriptions. It can be broken down into three sections or different groups. The “groups” are categorized by experience level - entry-level, junior and senior.
Entry-Level Data Scientist
Entry-level data scientists are industry newcomers. These people still don’t have a clear idea of what do data scientists do, at least actual work-wise.
A typical entry-level data scientist is a person who has just acquired his or her degree and is now trying to find their first real job as a data scientist. Some entry-level (beginner) data scientists already have some experience with working in an actual company, but it’s a rather rare occurrence. The usual scenario is that these professionals have just gotten into data science jobs and are “fresh out of the oven”, and are reading through data scientist job descriptions to get a feel for what’s to come.
This probably isn’t a surprise, but beginner data scientist jobs mostly revolve around learning. When an employer hires an entry-level data scientist, they know that this person is still completely fresh, and thus choose suitable and appropriate tasks. Furthermore, most companies that hire this tier of data scientists have special training programs in place. These programs are designed to help beginners understand their future work quicker and more properly, and get into the vibe of working in a team-based environment.
When it comes to the actual tasks of beginner data science jobs, they mostly revolve around using machine learning for daily tasks, performing various analyses, extended certain parts of the company's data, and so on. Keep in mind, though, that all of the different types of data scientist jobs involve some of the same tasks and requirements - it’s just that their complexity and magnitude are quite different.
When it comes to the requirements of entry-level data scientist job description, one of the main ones would be to possess adequate, relevant education. Proper education is very important since most potential employers aren’t even going to look your way if they see that you haven’t finished any specific studies relating to data science.
As time goes by, more and more people turn towards online learning alternatives instead of taking the traditional route of enrolling in a college or university. While that’s all fine and dandy with some specialties out there, data science jobs are a bit of a different story - in this field, most employers still require employees to have specific qualifications acquired in proper, established learning institutions. This is mostly because data science is a very complex and multi-layered area of study, and requires a lot of different approaches. But that doesn't mean that you can't learn from online courses on the side. And if your official studies are eating up all of your finances, you can apply for a data science scholarship for online classes.
Other than education, if you’re looking for a beginner data scientist job, you will find that most of these job descriptions mention motivation, passion, hard work and a wish to further your knowledge as some of the main requirements for the job. This isn’t just empty talk! Although these features can be assigned to most jobs out there, things like hard work and logical thinking are essential in the field of data science.
Career Path Options
As a data scientist job descriptions, your career path is going to be quite straight forward. Your ultimate goal should be to advance through the ranks of data science jobs and eventually reach the role of a senior data scientist. Going from barely knowing what do data scientists do to become the ultimate expert of the field can be a daunting journey, but it’s one that will yield worthy rewards.
As you advance through the ranks of data scientists, so will your salary, job opportunities and benefits, and various other perks that will unlock along the way. Even though the career path itself isn’t an easy one, many people still choose data scientist occupation because this field offers great job stability - once you’re in, you can be sure that you’ll always find a job (assuming that you put in the work and the effort, of course).
As a beginner data scientist, you will see that most of the data scientist job descriptions offer the least amount of money out of all of the three ranks mentioned in this tutorial. The reasons are pretty obvious - you’re still trying to get a feeling of how the industry works and learning all of the different tasks that you’ll have to perform within your new workplace.
Ziprecruiter.com estimates that the average yearly salary of entry-level data science jobs should be around $69,000. All things considered, that’s a pretty good salary when you’re just starting!
Junior Data Scientist
Junior data scientists are the most common group of professionals that you’ll encounter within this area of expertise. These professionals no longer wonder “what do data scientists do?”, but they still have a lot of learning to do to reach the ranks of senior data scientists.
The junior data scientist job description requirements are quite straightforward - you have to be able to work on your own, without too much supervision from your peers and seniors. Naturally, when you just begin a job in a new company, there are going to be people who show you around and teach you the general tropes of how things work and what it is that you’ll be doing. However, most junior-level data science jobs are going to require you to learn fast and start working on your tasks ASAP. This is mostly because junior data scientists tend to already have some experience with past work, and have the general knowledge of what it is that they’re supposed to do.
Same as for the entry-level, the junior data scientist requirements revolve around having a proper education, being motivated and passionate about what you do, and working hard to improve yourself. Different from the beginner group, though, junior data scientists focus less on learning, and more on executing their given tasks. Naturally, the learning continues more quietly - it’s just that the emphasis shifts.
One more important feature that a junior data scientist should possess is the ability to make the right decisions under pressure. Even though it might not be one of the specific data scientist qualifications, it’s still an integral part of the data scientist job description.
Furthermore, a lot of companies offering junior data science jobs are going to require their candidates to already have some experience working with data science. Some companies require more, others - less. The fact of the matter remains the same - if you want to get a decent junior data scientist job, you have to have collected at least some sort of prior experience with this profession.
A junior data scientist job description will mostly involve being able to work in a team, possessing a passion for data science and data analysis, creating specific systems and tracking how they perform over time, data mining and so on. These requirements are going to be an addition to the ones that beginner data scientists have.
Since you’ll have to possess a great understanding of machine learning and the common data science tools, one of your main responsibilities will be to apply them in your everyday tasks. This way, you will not only be able to practice your skills, but also show your new employers that you’re suited for the job.
Career Path Options
One of the main perks of being a junior data scientist is that you will rarely encounter a situation in which you couldn’t find suitable data science jobs. This is where this tier is quite superior to the beginner one - while some companies might want to avoid hiring people without prior experience, most of them are still looking for junior-level data scientists.
This is one of the main perks of having junior-level data scientist qualifications - you don’t have to stress about your future career path. If you’re passionate about the subject (at this point in your career, you should already know whether or not this is the career path for you), you will almost always be able to find suitable work opportunities.
The salary that junior data scientist job descriptions offer is a confusing topic for most. This is because the junior group of these professionals is somewhat large, and varies quite heavily in the level of skill. However, with that being said, Glassdoor.com still provides an average estimate of the salary of junior data science jobs.
According to the site, junior data scientists should make around $86,600 per year. That’s quite above the average yearly pay in the US!
Senior Data Scientists
Finally, we have a senior group of data scientists. As you might have already figured out, senior data scientists are the leading experts in their field - these people have dedicated their lives to data science and machine learning, and have spent years perfecting their skills and increasing their knowledge.
The requirements that are found in senior data scientist job description, as you might expect, are pretty much self-explanatory - you have to be an expert in the field of data science, having dealt with and learned all of the different tools that this field requires. Senior data scientists should have a thorough knowledge of not only their particular specialty but also everything around it (for example, they have to be awesome data analysts, too).
I probably don’t even need to mention this, but the above-stated requirements come in addition to having a higher education in a field that relates to data science and being super-experienced with past work. To add to that, data science jobs require to possess great knowledge of data visualization tools, know everything about using query languages in the processes of data science, have amazing statistical skills, and so on. That’s a lot of skills to possess!
Senior data scientist job descriptions are going to task you with some of the most difficult assignments within the company. Furthermore, most senior experts have to manage to work on their tasks and also simultaneously help company newcomers learn their way around faster. Working on multiple difficult tasks is challenging enough, but having to then also teach newbies the intricacies of data science within the company is on a whole new level!
Career Path Options
As you might have guessed, senior data science jobs don’t offer many more “upgrades” rank-wise. However, even after you become a senior data scientist, things like salary, job benefits, paid vacations and huge projects will continue to increase and upgrade. So, if you’re worried about not being able to grow as a senior data scientist, there’s no need - there will be plenty of room for improvement!
Senior-level data scientist job descriptions offer the highest salaries out there. And that makes perfect sense, too - if you’ve spent the majority of your life learning and progressing through the ranks of data scientists, it is only fair that your salary represents that!
GlassDoor states that the average base annual pay for the senior data specialist should be around $134,200. That’s a tough number to comprehend, but it’s a great indication as to why data science jobs have become increasingly more popular over the recent years.
Are You Ready For a Data Scientist Path?
By now you not only know the various data scientist requirements but should also be more determined to learn data science and progress through your future career path. Be sure to check BitDegree courses to learn more. Best of luck!