Data Scientist Job Description: What to Expect in 2023
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It’s been said that you can’t improve something that you can’t measure. And so, in today’s digital landscape, where every interaction becomes a measurable data point, data scientists are increasingly in high demand.
The job of a data scientist now ranks sixth on U.S. News’ “100 Best Jobs” list. And it’s easy to see why. Data scientists solve real-world problems, which is why many data scientists (even entry-level ones) make more than a hundred thousand dollars a year. From healthcare to tourism, almost every industry has data that needs to be analyzed.
Want to study trends in patient data to improve success rates for cancer treatments? You’ll need a data scientist for that.
Want to track and predict the likelihood of certain mood disorders in a specific community? You’ll need a data scientist for that too.
Do you love traveling and want to help tourism companies offer better travel experiences by using customer sentiment analysis? Better hire a data scientist.
These are just a few examples of niches that need data scientists, and the list goes on and on.
Want to learn more about what a data scientist does, what skills are required, and what data scientists can expect to make? Then keep reading.
What Is a Data Scientist?
A data scientist is someone who can find meaning in data. Everyone solves problems with intuition, imagination, logic, or knowledge. Data scientists solve problems through data, and they provide solutions to business challenges and help with data-driven business strategies.
What Does a Data Scientist Do?
Let’s answer this question with a hypothetical. Say that Cory is a data scientist working for McDonald’s.
To help improve customer experience and sales, Cory builds models using data. McDonald’s is looking to market a new, more premium, healthy choice meal. And so Cory is trying to answer the following question: “What’s the most enticing meal combination that still meets health guidelines?”
Here are the steps that he’ll take to answer this question:
In the discovery phase, Cory will try to understand the nuances of the question he’s trying to answer. So he’ll ask questions about their target customers—including demographic information, customer segment information, their pain points, and why they like to eat at McDonald’s.
Then, Cory will look for data, which can include combing through McDonald’s past sales records to pinpoint patterns related to “healthier” items. Cory will also look for guidelines that qualify what constitutes a “healthy meal.” Finally, he’ll go through McDonald’s menus at different locations, collecting nutritional information about their offerings.
All this data will be in its raw form, meaning it is unstructured and in silos. So before he can start to analyze this data, Cory needs to process it, which requires scrubbing and cleaning the data to make it consistent. Missing variables, wrongly recorded quantities, and incorrect values are just some of the gaps that Cory will fill.
Now that Cory has processed all of this data, he can integrate it into a unified hub of data points that can be analyzed.
Now, Cory will trim, supplement and refine the data. This will help him determine whether he needs to revisit previous steps. For example, has he checked for calorific values across different branches in the area? Does he need data on sales from ten years ago, when the definition of a healthy meal was different?
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Exploratory Data Analysis (EDA)
Cory will also screen the data to ensure that certain assumptions (i.e., that cheese is an unhealthy component) are valid. Finally, Cory will label his data across various classifications—such as continuous, discrete, and categorical—which will dictate the techniques he uses to analyze this data.
Implement Data Science Techniques
Cory will then use tools to cull insights from the data. These tools could include:
ML tools can automate parts of the project’s life cycle, including collecting and cleaning data.
When there is a vast expanse of data, Cory might prefer visualization for the intuitive recognition of patterns. Instead of scanning pages of historical sales data numbers, Cory can produce graphs to identify relationships.
AI is an umbrella term for machine learning, deep learning, etc. Cory can use simple or advanced AI tools to analyze large datasets of the menu items, ingredients, and nutritional values.
With all these tools, Cory might create predictive modeling tools to cover various food combinations.
Measure, Analyze, and Improve Results
Cory will now measure the results that he’s generated and analyze these results to produce insights.
He might find that the ways of communicating nutritional value need to be changed to encourage healthier choices. For example, younger folks may prefer an info-packet that includes dietary values to make their decisions.
Once he’s measured and analyzed his results, Cory might also find ways to improve his original techniques. For example, he might consider a longer time range, different variables, or a “micro versus macro” approach.
This is all part of Cory’s data scientist job description:
Data Scientist Job Description
No single data scientist job description can be considered “the” job description. Each organization requires different things from their data scientist teams, so the qualifications and requirements may differ depending on who you work for. However, there are some commonalities across most data science job descriptions. Here are two examples from Lego and Twitter, two completely different companies that both need data scientists:
Data Scientist Qualifications
As you can see in the two previous job descriptions from Lego and Twitter, the following degree requirements are preferred:
- Bachelor’s degree in computer science, mathematics, or an adjacent field like economics, information management, statistics, or business information systems.
- Post-graduate degree in business analytics, data science, big data, etc.
- Advanced degree such as a Master’s or Ph.D. in operations research, data mining, machine learning, electrical engineering, etc.
Note that both job descriptions include the phrases “or other quantitative description” or “in relation to…” This is likely because data science, as a field, is rapidly expanding, and many companies see core degrees as a preference, not a prerequisite, if you can demonstrate your proficiency in other ways.
Data Scientist Technical Skills
The job descriptions from Twitter and Lego both require certain technical skills. Robert Chang, a data scientist at Airbnb, advises aspiring data scientists to not worry about learning everything. Instead, he advises that you focus on just learning the skills you’ll need for the job that you want. However, all data scientists should learn R or Python and some SQL.
Let’s break down some of the technical skills that both of these jobs require:
This means collecting, organizing, analyzing, and interpreting data to present key findings. Statistical techniques include hypothesis testing, standard deviation, regression, etc.
This will help you at every stage of your statistical analysis and during other funnel phases. It can help automate data collection and scrub data with minimal manual work. You can also use machine learning techniques to analyze data and train models that will make predictions based on the patterns and networks in your data set.
A data scientist job description will include one or more of these programming languages—R, Java, Perl, SQL, Python, C/C++, etc. These will add nuanced analysis to your data sets. You can simplify your data sets and get to your solution faster if you can write programs in these languages. Consider enrolling in free online programming language bootcamps and certified courses offered by universities.
Most data scientist job descriptions require some familiarity with Hadoop, Apache Spark, and NoSQL platforms. All of these allow data scientists to process data more efficiently.
Mastering Tableau, or a few of these free data visualization tools, will help you share your data insights through graphs, charts, and other visual aids.
Spiffy data visualizations aren’t enough to communicate your findings. You’ll also need to tell the story of your data science process to give your team actionable insights.
Data Scientist Soft Skills
Here are some soft skills that are essential to any data science role:
Data scientists with strong analytical skills help companies save time and money. Analytical thinking will help you understand the question at hand, identify what you need to solve it and recommend a course of action.
When working for a corporation, having a larger perspective on the goals of the business is crucial for any data scientist. This means having some business acumen. If you don’t know the context of the problem you’re trying to solve, then you won’t be able to generate valuable insights.
Data science is all about using data to find a solution to a problem. Therefore, you need to have the necessary thinking skills and natural curiosity to get to the root of the question and apply multidisciplinary (statistical, business, analytical) approaches to the issue. If you cannot identify and analyze the question, no amount of programming skills or data sets will help.
Data Scientist Salary
What you make as a data scientist will depend on a range of factors, including your qualifications, your job title, company size, industry, and region.
But the biggest factor in determining your salary will usually be your level of experience. According to the 2021 Burtch Works Study on DS jobs, the median salary ranges that most data scientist job descriptions will reflect are:
- Entry-level data scientist (0-3 years of experience): $90,000
- Mid-level data scientist (4-8 years of experience): $115,000
- Senior-level data scientist (9+ years of experience): $145,000
- Data scientist managers:
- Level 1: $155,000
- Level 2: $200,000
- Level 3: $275,000
How To Become a Data Scientist
Now that we’ve looked at the “what” and “why” of data science, let’s look at how to actually become a data scientist. Other than degree qualifications, you can become a data scientist faster by doing the following:
Build a Strong Foundation
A firm grasp of mathematics and statistics is an excellent starting point for any data scientist. But you don’t necessarily need a formal degree to build this foundation. With so many online courses and bootcamps at your disposal, you can cover all the prerequisites with a structured study plan. Check out Springboard’s data science career track bootcamp or its data science prep course to get hands-on experience.
Expand Your Skills (and Vision)
To evolve your craft, you have to immerse yourself in the data science field (and in a little bit of data analytics, perhaps). Besides courses, you can engage with the data science community through events, webinars, and summits. Keep expanding your horizons as per the market demand. As you saw above, many data science roles actually recommend this in the job description:
Another way to expand your skills is to collaborate with other data scientists on open source projects, either independently or through camps. Get inspired by these data science projects.
Create a Portfolio
A portfolio is a great way to showcase your work. You can host your portfolio on a simple website or blog that demonstrates your expertise in a clean and concise manner. You don’t need to feature every project you’ve ever worked on. Instead, highlight the real-life benefits of a few projects to show potential recruiters how you can contribute value to their organizations.
Apply for Entry-Level Jobs
Everyone has to start somewhere, and this is also true for data scientists. Your first data science job will hopefully set the tone for the rest of your career, help you add to your portfolio, and expand your network. (Related Read: 3 Key Steps to Landing Entry-Level Data Science Jobs)
FAQs About Data Science as a Career
Here are our answers to your most frequently asked questions.
Can You Become a Data Scientist With No Experience?
Yes! Take stock of your current knowledge in the data science field, and supplement it with free courses and webinars targeted toward beginners. You should also make a structured plan that will help you meet the requirements of an entry-level job and connect with experts to help you break into the field. You can check out this guide to becoming a data scientist with no experience.
Do You Need a Degree To Become a Data Scientist?
No. You can learn about data science and explore its various branches through online courses and professional certifications.
But if you go this route, you need to be more strategic with your learning process. Data science is a sea of different skillsets and goals. Here is a guide on how you can learn data science without a degree.
Is It Hard To Become a Data Scientist?
It depends, but the short answer is NO!
A data scientist can specialize in data visualization, business analysis, engineering, and many more. The proficiency required for each of these differs. Realizing whether data scientist is hard is like an exercise in data science itself.
If you were Cory, you would define your career goal, acquire data, and tweak it to understand whether you have amassed all the resources and skills to become a competent data scientist.
You can read about a day in the life of a data scientist at Google to see what data science employment in big corporations looks like.
Since you’re here…
Curious about a career in data science? Experiment with our free data science learning path, or join our Data Science Bootcamp, where you’ll only pay tuition after getting a job in the field. We’re confident because our courses work – check out our student success stories to get inspired.