Harvard Business Review called the position of data scientist the 21st century’s sexiest job. No wonder that in the IT industry, data scientists rank among the highest-paid professionals. However, there are three key immersive data scientist salary factors that a person should take note of in order to ensure he/she gets the desired salary. It’s important to note here that despite the attractive salary quotient, the field of data science isn’t an easy one. So, unless one is really driven to work with data and ready to work hard to acquire the requisite knowledge and skills, he/she shouldn’t consider joining this domain, especially when driven only by the high salary factor.
For example, the top skills you will need to make it big in the field of data science are Python, statistical analysis, Java, and Hadoop.
Before we take a look into the key immersive data scientist salary factors, it’s important to note that geography has an enormous influence on the income of data scientists. According to glassdoor.com, a data scientist in the United States earns an average salary of almost $118000. However, the figure is about $93000 according to payscale.com. It goes without saying that data scientists with high experience can expect to earn high salaries. When beginning his/her career as a fresher, a data scientist can look forward to earn almost $90000 as an annual salary. Those having 5 to 10 years of experience can earn a take-home pay of about $109000.
Data scientists with over 10 years of experience can expect to get an annual salary of $124000.
If you wonder how geographical location influences the pay of data scientists, some figures from the US would make the picture clear. Data scientists in San Jose earn 28% more than the national average, while it’s 18% more for those in Palo Alto. Compared to the national average, San Francisco pays 21% higher salary to data scientists, while for New York, the figures stand at 6% higher.
Some say the coveted job of data scientist may be losing some of its attractive quotients as salaries for the position has started to plateau. They say with growing competition and flattening salaries, the prospects that ere once stellar may no longer be so for data scientists. But despite a slowdown in the field, the position of data science is still a lucrative one, much more than many other posts in the IT landscape, which still makes it a coveted one.
If you have your eyes set on the domain of data science, you should know about the three key immersive data scientist salary factors. You may call these three areas that you need to work upon to become a better data scientist, and thus, earn a fat pay packet. But before we go further, it’s important to know for aspiring data scientists that they don’t need to know everything to become a successful data scientist. You should plan your career realistically. After all, there is an almost unlimited number of machine learning/data science topics, but you can actually learn only a handful to begin with. Despite what some self-proclaimed experts and unrealistic job applications may say, you don’t need to possess complete knowledge of every algorithm or have 5 to 10 years of work experience to become a practicing data scientist. So, instead of feeling overwhelmed by the huge number of topics you believe you must learn, you should ideally start with the basics and build upon it from there.
So, let’s delve deeper to take a closer look at each of these three key immersive data scientist salary factors.
When you take an academic approach to data science, you may develop the inclination of writing code that only runs once. Additionally, you may have a tendency of developing difficult-to-read code without a consistent style, having a lack of documentation, and hard-coding particular values. Such practices indicate your solitary primary objective — to create a data science solution that works just a single time for a particular dataset.
An instance could be when you work with data that initially came in 10-minute intervals. Since you didn’t think about making your code easy to read or flexible to varying inputs, you would be at a loss when data starts coming in, say 5-minute increments. Had you written the code from a software engineering viewpoint, it must have been extensively tested with a lot of different inputs. Additionally, it would be well-documented, adhere to coding standards to make it easy for other developers to understand it, and function within an existing framework. What this means is that instead of writing code as a data scientist, you should approach the task as a software engineer.
Successful data scientists write code using software engineering best practices, thus ensuring their model is robust to be deployed and fits within an architecture.
If you wonder how you can do it, you should know that nothing beats practice for learning technical skills. If your present job gives you the chance to do it, you can easily learn and hone your coding skills with practice. If not, you can take up collaborative open-source projects. To work out solid coding practices, you may even read through the source code for GitHub’s popular libraries. Finding a community of software engineers and data scientists, who are more experienced than you and from whom you can get advice, is yet another efficient way. Using these modes, you may end up learning a number of practices including:
- Following a coding style guide
- Writing unit tests
- Thorough documentation of code
- Writing functions that accept varying parameters
- Refactoring code to make it easier and simpler to read
- Getting the code reviewed by others
Additionally, you may use linting tools, which are available in plenty and help you to check if your code follows a coding style. You could even focus more on writing efficient implementations rather than using brute force methods (such as opting for vectorization in place of looping). But it’s important for you to realize that you can’t change everything overnight or all at once. Thus, the key is to focus on a small number of practices and make them habits integrated into your workflows.
Now that you know about one of the key immersive data scientist salary factors, let’s move onto the next one.
One difficulty you will face in the domain of data science is scaling a predictive model or an analysis of large datasets. Like many others, you may not have access to a computing cluster and won’t want to shell out a large sum for a personal supercomputer.
To solve this problem, you may tend to apply the new methods you learn to small, well-behaved datasets.
The only problem is that real-world datasets don’t have cleanliness limits or adhere to an exact size, which makes it important to use different approaches to resolve problems.
One solution is to use a remote instance, like through AWS EC2, or even multiple machines. However, this would mean learning ways to link to remote machines and mastering the command line (because you won’t have access to a GUI and your mouse on your EC2, for instance).
Another problem you may encounter is when handling larger datasets the memory of the machine. One way is iterating through a dataset one portion at a time, by breaking one large dataset into several smaller pieces, and using tools like Dask or Spark with PySpark to run the subsets through a parallel pipeline. You won’t need a cluster or a supercomputer for this approach as you can parallelize processes on a personal machine using multiple cores. Once you have access to additional resources, you can adjust the same workflow to scale up.
With the increasing use of AI across various industries, deep learning, which uses multi-layered neural networks, is going to be big, say some experts. Your present position may not require you to learn and apply deep learning as you can deal with problems by using conventional machine learning techniques such as Random Forest, etc. However, you should know that not every dataset that you need to work upon will be structured in neat columns and rows, and when they aren’t, neural networks would be your best bet, especially for handling projects with images or texts.
Thus, familiarity with deep learning and being confident in implementing some of the techniques would let you handle a wider range of problems, which in turn would make you better placed to earn a higher salary as a data scientist. No wonder why deep learning features among key immersive data scientist salary factors.
Perhaps you now understand that key immersive data scientist salary factors are as much about your present skills and competence as well as your inclination to pick up new skills and techniques that you may not need now but can apply to future situations to solve problems. So, act accordingly to ensure these three key immersive data scientist salary factors as mentioned above work in your favor.