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Explore different Data Science career paths and how to get started

Data Scientist

How to Become a Data Scientist in 202X

Hi, my name is Shaohua. I’m the CEO and co-founder at WeCloudData. Many of you are reading this article because you’re either interested in learning more about data science or have made a decision to switch careers to the data science field. Read on, this career guide provides the useful bits you’re looking for.

The Data Science field has evolved

Data Science has changed a lot in the past few years. The data science field has been evolving rapidly. The Data Science you hear today looks very different from 10 years ago. Tools have changed, new frameworks have emerged, more companies going through digital transformations are starting to embrace data science, and of course, the job definition of a Data Scientist has evolved as well!

The good news is that data scientists today are empowered by powerful open source tools such as Python, Scikit-learn, Tensorflow, PySpark, Docker, and cloud platforms such as AWS, Azure, and GCP. The NOT so great news for job seekers is that the market is getting more crowded and competitive so the entry bar has risen quite a bit. Job candidates are required to constantly learn new things on the fly and to be able to showcase their hands-on experiences.

Job postings are still quite confusing

Some of the most common complaints that we constantly hear from job seekers is that many companies post data jobs that are utterly confusing. It feels like many companies don’t really know what they are looking for. It’s common to see a job description (JD) that asks for 20+ technical skills while the real job may just need 5 key technical skills mentioned in the JD. It makes the job look daunting as well as confusing to some of the junior talents.

This is the sad reality because many job descriptions are written by HR and recruiters who don’t know data science deeply. The data scientist profiles are also not well defined as companies have yet to figure out the right hiring strategy.

The good news is that it’s getting better! Over the years, many companies have been going through digital transformations and they’ve learned from those mistakes. Companies don’t rush to hire a team of all-star data scientists anymore. They’ve seen more failed examples and started to realize the importance of building a proper data science team instead of just hiring unicorns.

Data Science jobs are getting more specialized

I’ve worked in the data science field before data scientist was coined as a sexy job title. Seeing the changes that happened in the data science field in the past 15 years has been a blessing. I noticed that companies have been becoming more rational when it comes to hiring decisions and it’s partly because companies have more best practices to learn from. Some companies have tried and failed and learned from those failures.

Executives start to realize that to achieve the truly data-driven goal for an organization, they need to build a data science team comprised of different roles such as data scientist, data engineer, machine learning engineer, as well as data or business analyst. And for organizations that are building data products, data-driven product managers also play a key role.

So the good news is that for someone who’s considering a career in data science, different data roles are better defined now and one has more options than before.

Why is Data Scientist still the sexiest job?

If you’ve paid close attention to the data world, there are some emerging trends. For example, some data scientists work just like a data analyst. We also see the trend of the two fields merging in the near future as data science gets more specialized. Another trend is the rise of data engineering. Many companies start to realize that having data scientists who are more specialized in machine learning and data visualization is not enough. Data plumbing is even more important for companies at an early stage of the data journey. But ultimately what makes a data scientist the sexiest job is the following:

  • Companies are facing unique data problems even if they are from the same industry. They structure data differently, face different business challenges at different stages and hence different data problems. Data Scientists are required to help tackle tough business challenges through data plumbing, understanding, visualization, predictive analytics, and prescriptive analysis. These data problems usually have a big impact on company’s business bottom lines.
  • One important aspect of digital transformation is the shift in culture. Companies are increasingly becoming more data driven. Business teams are required to become more data literate and be able to ask more sophisticated data questions. Data Scientists play a key part of this culture shift. They are the internal data experts and advocates that help shape unique culture around data.
  • Data Scientists can help insert influence in product design and smart decision making. Though the work they do don’t always lead to big changes in business outcome, the measurable and incremental gains can often help companies win in the long run.
  • Data Scientists work closely with business teams and the work they do are often more recognized by the business teams. Due to this nature, data scientists tend to have more glory than other data professionals such as data engineers and data analysts.

Though there are many good reasons to consider a career in data science, it doesn’t mean everyone should become a data scientist. The job is also not for everyone. Choosing the right fit is the most important thing to consider. We suggest you read our other career guides as well. If you are also curious about careers in BI or Data Engineering, read our BI Career Guide and Data Engineer Career Guide.

About this career guide

Before you start to learn data science, do some research. This career guide will help you better understand the Data Science career path and whether or not it will be a good fit for you.

If you decide Data Science is a good fit, then WeCloudData’s Data Science Bootcamp program can help you launch your Data Science journey and land that dream job.

This guide will help you navigate the skills and job requirements as well as career paths in the Data Science space. There are many roads that can lead to a Data Scientist job and most of you will likely have a unique background, story, and reason for breaking into the field. There’s no one-size-fits-all, but these general points can help act as a guide for your journey into Data Science.

First, we will help you understand what Data Science is, some career paths associated with Data Science, as well as the Data Science job market. We will then explore the learning path and tools leveraged by Data Scientists. Finally, we’ll wrap up with some sections to help with your job search and explore the many ways WeCloudData can help in your Data Science journey including mentorship support and advice.

Check out other career guides

Our Career Guide provides all the resources you will need to help you get started in navigating data careers. We include free resources, guides, and tools to help you get started.