Top 5 Data Science Career Paths
Published: Nov 15, 2021
Today, companies across numerous industries employ and rely on data scientists to grow their businesses. In general, data scientists are responsible for collecting and analyzing raw data, using data to gain insights into business processes to help achieve various goals. For example, a data scientist working in application development might collect data on how users interact with an app and use this to improve user experience. Or a data scientist in testing might monitor the automation testing strategy for any faults in tests.
With so many employers relying on data scientists, data science has become one of the fastest-growing professions. And below are five of the best data science career paths for recent graduates and young professionals.
1. Data Engineering
Data engineers typically work closely with the rest of the data team to distribute the appropriate data to the right people. They do this by building and maintaining the infrastructure that allows data sets to be sent between members of a team. They also process raw data from the source to make it more coherent for data analysts. This role usually requires an in-depth understanding of how data is stored and how the particular systems of businesses work. There is generally a lot of room for progression in this role.
2. Data Analysis
Data analysts are responsible for looking at data from an area of the business and using it to answer questions. They then communicate these answers in a clear and accessible way to the rest of the team. For example, a team that has adopted a new .ai domain as part of a sales strategy might use a data analyst to determine the success of this campaign. On this project, you may be collecting sales data from the website compared to those of the previous domain. This role tends to be an early career role, but there is room for progression.
3. Machine Learning Engineering
Data sets are crucial to developing AI and machine learning software. AI has a huge range of applications in business today, so you won’t be limited in terms of project variety. Machine learning engineers work on creating software that can learn and apply its knowledge. Understanding data sets is crucial to the repetitive processes that help the machine to learn. For example, automation artificial intelligence combines RPA (robotic process automation) and artificial intelligence to allow a business to quickly and effectively automate its processes. This is a niche role compared to general data positions.
4. Business Intelligence Analysis
Business intelligence analysts work with data on market trends. They can also work with business processes, using data to solve problems within the business. Some of the most common projects for a business analyst are: using data to solve identified problems within the business, analyzing business processes to improve efficiency, and presenting data to team members effectively and clearly. The main goal of a business analyst’s work is enabling efficiency within a business.
If you’ve got a great business idea, why not go it alone? Being able to collect data from your sales and marketing campaigns will save you time and labor costs that could be crucial to a new business. Beyond that, you may be able to use your data skills with your new product or service. If you have engineering know-how, then you’re well on your way to being able to fill a gap in the market with new software. There is clearly more risk in this career, but there is a lot of scope for progression and a variety of work-based experiences.
The first thing you’ll need to pursue a career in data science is a great resume. Learn how to build a data science resume and tailor it to the positions that you’re applying to. Make sure to include any relevant experience, education, and skills that make you stand out from the crowd. Next, if you find that you need to gain experience in the field of data science before applying for full-time jobs, try applying for a data science internship first. Finally, to begin compiling a list of companies to target that hire data scientists, this ranking is a good place to start.
Tammy Wood has been involved with SEO for two decades. Her current role is Director of Technical SEO, for Automation Anywhere, an intelligent automation ecosystem that offers Google cloud RPA. While not chasing keywords Tammy enjoys reading, buying shoes and writing articles about both RPA and SEO. Here is her LinkedIn.