A data scientist is someone with solid quantitative skills and strong analytical abilities who uses those skills to solve complex business problems through predictive modeling using statistics, mathematics, and technology.
With ever-growing amounts of structured and unstructured data at our disposal, there are limitless applications for statistical models and software programs like SAS or SPSS. Businesses are collecting increasingly large datasets that they organize, process, and analyze in order to make more informed decisions.
Data science has become one of the hottest career paths in recent years. There are now over 1 million job openings worldwide for information technology (IT) professionals, according to Monster. With IT employment rising faster than average, becoming an experienced data analyst is a great way to stay ahead of the curve.
Business owners typically look outside their organization for insight and direction when it comes to improving efficiency and productivity. They may turn to external sources for answers such as market research firms, academic institutions, and consulting companies. In these cases, having a resume full of impressive achievements is important!
Fortunately, we have some helpful tips here for you to develop your own successful data scientist resume. Read on to learn how to write the best possible data scientist resume.
Reflect on your skills
It’s not enough to have strong writing and math skills; you need to know why those skills matter to an employer. And while hard-skills such as programming or statistics speak directly to a position’s responsibilities, soft skills like leadership, communication, and organization are just as important to success at work.
So consider taking some time to reflect on these types of skills and determine which ones you are best in. You can even create a list of them according to order of importance to ensure they get included in your own career success.
Whatever you do, don’t underestimate the value of being able to communicate effectively! Too many people fall into the habit of talking only about technical topics, so becoming aware of non-technical fields and learning how to apply appropriate levels of jargon from those areas is very valuable.
Generalists who learn several different domains well are highly valued by most companies, so practicing this skill will set you apart.
Experience and education
In this era of technology where almost everything seems automated, there are still many positions that value practical experience over formal education. Even in industries that require advanced degrees, most employers will give you at least some credit for past experiences as long as you can prove you learned something from them!
Data science is no exception to this rule. While it may seem like an easy job for someone with a bachelor’s degree in statistics or computer science, it takes a lot more than just those two fields to be successful.
That’s why being able to show off examples of how you used math and logic to solve business problems is so important. You don’t need to have a MSc in Statistics to work as a data scientist, but being able to describe complex statistical concepts clearly and succinctly is a very appealing way to showcase your talent.
Think about branding
A strong personal branding strategy is one of the most important things you can have as an entry-level professional in data science or any other field. Your personal brand will speak for itself – it does not need validation through others, nor do you have to prove yourself to it!
Data scientists are increasingly needed across various industries, so having a well-developed personal brand that sets you apart from the rest is a valuable asset.
It’s easy to develop your personal brand in fields like business, marketing, technology, and communication. By acting with integrity, being thoughtful, and keeping yourself organized, people will perceive you as intelligent and self-confident.
These qualities are attractive to employers who look for them in potential candidates. You want to be known for your competence, not your ability to put on airs at times.
If you’re already developing your personal brand, great! Keep doing that and try to emphasize concepts such as efficiency, productivity, and quality over flashy ones like charisma or prestige.
Market your skills
A great way to market yourself in this ever-growing field is by emphasizing how you applied quantitative techniques to solve business problems. If you’re very good at marketing, advertising, or analytics, include those points in your resume!
Data science includes both hard and soft sciences that can be mixed together to achieve an end result. Emphasizing numbers and mathematics in particular makes sense as they are always needed in data analysis.
But beyond just math, people who do data science apply theories like psychology, sociology, and organizational behavior to determine what factors contribute to success in their area of expertise. These concepts can also play important roles in achieving career goals for someone with no formal education in these areas.
To show off your talents more effectively, consider using examples that emphasize applications outside of pure number theory. For instance, instead of saying “Analyzed trends in customer buying patterns,” say “Created predictive models for online shopping sites.”
And while it may sound cliché, being passionate about what you study will help get you interviews and opportunities. If possible, try to find some proof of your achievements, such as through past projects, testimonials, or presentations.
Where do you find that job, though?
Looking for work in this field can be quite difficult, which is why most employers do not require a formal degree in it. Instead, most companies want you to have at least one year of experience under your belt as well as certification or licensure.
Companies are becoming more flexible about what credentials they accept, however. More than half now ask only whether you have certifications rather than requiring a specific one. There are many ways to gain such qualifications so don’t feel like you need to pick just one!
Data scientist positions are in high demand, so start looking early if you’re interested in working in this area. By starting your search earlier, you’ll have some time to find employment after graduation.
Prepare for interviews
After you have designed your resume, it’s time to prepare for your next round of job interviews! While most employers will spend some time talking about how their company values fit with yours, they will more than likely focus most of that conversation on why you are a good candidate for the position.
They will ask questions like: “Tell me something about yourself,” or “What do you know about our organization?” If you’re not sure what to say, don’t worry! There are several common topics that can help get the ball rolling.
Some examples include discussing your career goals, mentioning past achievements, telling them who you are, and explaining what makes you qualified for the position.
