Data Science Careers: How to Start and Build a Career in Data Science
Careers in data science can offer a wealth of opportunities. Graduates entering data science careers have become increasingly in demand as businesses recognise the need for data-led solutions in their daily operations. Those with a keen interest in problem-solving, programming, mathematics, big data and more will find a variety of data science pathways to follow. If you want to build a career in data science, read on.
What do data science careers entail?
In recent years, the role of data scientist has become integral for data-led organisations, particularly those that leverage customer information and analytics to make smarter business decisions. Data scientists help them achieve this by parsing large amounts of data and transforming it into relevant, up-to-date and actionable insights.
There are numerous roles in data science careers – from software engineer to business analyst – with many people starting as a junior or associate data scientist and then working their way to mid-level, senior and managerial level data scientist.
In terms of essential skills, Gartner explains that data scientists must possess strong:
- collaboration and teamwork capabilities
- analytical and decision-modelling skills
- data management skills
How to prepare a data science resume
While data scientists are in demand across Australia, roles remain competitive as the industry becomes an area of significant strength according to the Department of Industry, Science and Resources. To stand out in this field, your data science resume must be well curated and clearly showcase your diverse skill set.
“It is crucial that you have a solid combination of coding skills, a wide knowledge of analytical models and an understanding of the data environments,” says Dr Michael Baron, Program Lead for the University of Adelaide Master of Data Science.
You should draw on your previous roles and use the knowledge you have acquired across different industries to demonstrate why you will thrive in a data science career path.
“When you begin a career in data science, focus on the jobs and industries where you were previously employed in non-data science capacities. Industry knowledge is always a valuable competence that employers seek.”
Tips to prepare for data science interview questions
Once you have progressed beyond the initial application stage, you will be required to interview for the position. Take the requisite time to prepare and research some common data science interview questions - particularly if this will be your first job in data science.
Here are three suggestions that Dr Baron says can set you apart from other candidates.
1. Conduct extensive research
You may have already researched the business prior to submitting your resume, but an interview will involve two-way communication where the hiring manager may ask questions related to the role and data science at large.
“Do extensive research on the role,” Dr Baron says. “The lion’s share of data-science positions are linked to specific industries and data environments. When in the interview, take every opportunity to demonstrate that you are familiar with the environment and keen to learn more.
“Look into specific tools and patterns that are common within the industry and take a brief overview of those tools and patterns to the interview so you can talk your way through them.”
2. Build out your portfolio
If you are already on a data science career path, you may have a relevant portfolio to share with the hiring manager. For newcomers to this area, Dr Barron advises that you use this opportunity to showcase your transferable skills and demonstrate your role in successful projects at previous jobs. This is also your chance to share any accomplishments from studies related to the field of data science.
“If you have engaged in data science projects as part of your studies, such as the University of Adelaide’s Big Data Capstone Project, then do share them in your portfolio,” he says. “It will provide you with an opportunity to showcase your skills.”
3. Be enthusiastic
While employers value applicants who can demonstrate their capabilities relative to the position, they will also look for candidates who show enthusiasm for the role. They want team players who are eager to build a career in data science and move the business forward.
“While every company's requirements will vary, including the hard skills that are essential to data science, organisations are keen to hire new starters who have strong written and oral communication skills,” Dr Baron says.
“They also want people who are able to work both independently and as part of a team. Similar to other entry-level jobs, enthusiasm, eagerness to learn and a solid work ethic are big factors in choosing a candidate.”
How to get promoted to leadership roles in data science
For those who are already in the industry and wish to carve out strong data science careers, moving upwards via promotions is the clearest path to leadership. While some may achieve this through hard work and persistence within the same company, others may use networking to advance their data science careers at other organisations.
Whatever your data science career path aspirations, developing a habit of continuous learning can be beneficial long term. Upskilling through study and gaining experience with new tools and technologies can position you as the ideal candidate for promotion.
“For someone who is already in the field of data science, it is important to keep adding new technologies, patterns and tools to your internal CV,” Dr Baron says.
“Upskilling with data science courses and projects will help you remind management of your newly acquired skills and abilities.”
If you are ready to begin your data science career or upskill for data science promotions, the University of Adelaide has online study options to suit your needs:
- Aspiring data scientists who wish to formalise their skills can undertake an undergraduate degree or a Graduate Certificate in Data Science (Applied) Online.
- Experienced data scientists can then move on to a Master of Data Science (Applied) Online in order to broaden their career potential.
Want to know more about the different types of careers in data? Learn about data scientists, data analysts and data engineers next. You can also book a call to discuss program options with one of our expert advisors.
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