How to get hired in data science or data analytics

Understanding the data science employment space in 2023

Demand and supply in data science and data analytics

Understanding roles in data science

Before moving on to the specifics of getting hired in data science and analytics, it is important to bust a few myths commonly held by people unfamiliar with the industry. 

Afterwards, we will provide a basic breakdown of what being a Data Scientist or a Data Analyst actually entails in terms of day-to-day tasks and responsibilities.

A day in the life of a Data Scientist

The specific tasks a Data Scientist handles will vary widely from job to job. There is also some crossover with tasks performed by Data Analysts. The key difference is that Data Scientists will go deeper into the numbers, might look into the future as well as analysing what has already happened, and will build models and tools to facilitate this. But to give you an idea of a typical day as a Data Scientist in terms of tasks, here is a breakdown according to Anaconda's findings: 

A day in the life of a Data Analyst

As a Data Analyst, you will probably be working as part of the Business Intelligence team in your company. Your role will be to act as an interpreter, taking questions or issues that are brought to you from various departments and going to the data for answers. Unlike a Data Scientist, you probably won't be creating algorithms, working with machine learning or doing modelling. Instead, you are more likely to be analysing what has already happened, and helping decision makers understand how the business is operating. 

A typical day for a Data Analyst will probably include most of the following:

• Receiving and understanding questions from different departments. You will need to clearly understand the business problem that is presented before you start investigating.

• Gathering and cleaning data. While collecting data will usually be straightforward (though this also depends on which industry you are working in), you will also need to clean the data. This is important as it ensures no incorrect data will remain that could distort your results.

• Processing the data. Here is where your skills in statistics will come into play. You will need to use tools like Excel, along with SQL, to effectively analyse the data and come up with answers to the questions you were asked. Some Data Analysts also do some programming at this stage with languages like Python, and R.

• Producing reports. Using business intelligence tools like Power BI, Tableau and Looker (which are core tools for Data Analysts), you will present your findings in a manner that is accessible and clear to non-experts.

• Spotting patterns. You may do some more in-depth work looking into patterns in the data that you have been working with.

• Meeting your fellow analysts, and other stakeholders. You will likely need to attend meetings with your colleagues working in business intelligence and other departments, although the volume of meetings will vary from role to role.

What skills are employers looking for?

There are a wide range of skills and competences that could fall under the bracket of data science. What is more, these skills will be applied within many different industries.

According to a study conducted by 365 DataScience that looked at 1,000 LinkedIn job postings for the US, data science jobs span 50 different industries. However, the spread is uneven: 49% are in IT and tech, 14% in financial services, 11% in staffing and recruiting, 4% in industry, 3% in healthcare, and 2% in defence. It is also important to note that only about 33% of job ads specifically require a Data Science degree, and 55% of the jobs posted are entry-level.

All of which means it is hard to pin down a precise set of skills that are in demand for Data Scientists or Data Analysts.

Unsatisfactory media coverage and online resources makes this problem worse. Articles on getting a job in data science sometimes list skills in outdated technologies. Others may be promoting skills that a particular outlet is selling courses in. You will also find articles that waste readers’ time by discussing skills applicable to virtually any job. In the best case scenario, you may find an article that provides a fairly credible representation of market trends and requirements… but then gives no evidence to back up the claims being made.

The difficulty of figuring out what skills to acquire for work in data science is a problem in its own right. Not knowing what experience is actually required was singled out as one of the biggest entry barriers by as many as 20% of students considering the data science profession in Anaconda's report.

The data behind data science skills

Faced with a question like this, Luke Barousse (aka DataNerd.tech) did what any data professional would do and dived into the figures.

In fact, he painstakingly analysed almost 1 million Data Scientist, Data Analyst and Data Engineer job postings to find answers to the questions on every applicant's lips:

• How many positions are there available?

• What qualifications do you need?

• What are the salary ranges?

• And, most importantly, what skills are employers looking for?

The story of how he built an app to gather this data is fascinating in its own right. But for now, let's focus on the insights he uncovered, because they are super valuable if you want to get hired in data science or data analytics.

datanerd.tech aggregates job listings and allows users to filter them for different criteria. It is based on over 1 million job postings from across the world (not just the US where Luke is based). While there is an understandable slant towards positions in the US (it analyses over 428,000 posts from the USA), other regions are covered very well. In terms of Europe, the platform analyses over 43,000 jobs in France, 33,000 in the UK,  29,000 in Germany, 6,000 in Czechia and 2,000 in Lithuania 2,097. In terms of other regions, there every continent (bar Antarctica) is catered for, with over 46,000 positions in India analysed, along with 23,000 in Singapore and 7,000 in Brazil. It is an excellent tool to explore, but we took the time to look into the most important questions job seekers might have.

Which job is most in demand?

In the 2010s, data scientist was frequently listed not just as the most in-demand job in the data field, but as one of the most in-demand jobs, period. Yet Data Nerd's 2023 analysis throws up an interesting finding. Data Engineers are now more in-demand than Data Scientists. Here's the breakdown they found.

Clearly, while there are plenty of positions available in each of these disciplines, Data Engineers are now most in demand.

Just a word of warning on these numbers. For many companies, these roles are somewhat new. Plus, the landscape is changing quickly in terms of how different responsibilities overlap. As a result, companies are not always accurate in how they describe the type of professional they are looking for. You might be reading a job posting for a Data Analyst position and realise halfway through that the company is actually looking for a Data Engineer.

First of all, this means we should treat statistics on the number of positions available as general guidance rather than set-in-stone rules. Secondly, if you find a job description unclear, make sure during the hiring process that you are both on the same page to avoid disappointment later on.

Do I need a degree?

According to the job postings analysed by Data Nerd, roughly two thirds of the positions for Data Engineers and Data Analysts mention that a degree is required. But for Data Scientists, more than 9 in every 10 postings mentioned the need for a higher education degree.

Granted, only going on whether a degree is explicitly mentioned as a requirement in a job posting is not foolproof. It is entirely possible that, even if a degree is not listed as a requirement, recruiters will still favour candidates with a relevant higher education. 

However, the fact that 33% of Data Engineer and 31% of Data Analyst job postings did not mention a degree is strong proof of a trend that industry experts have known for a while - degrees are no longer essential for landing a job. More hands-on forms of data science training, combined with a strong portfolio of projects, will often be looked on more favourably by employers than a degree. 

What form of employment is most common?

Across Data Engineer, Data Scientist and Data Analyst positions, there was a clear preference for full-time positions over part-time or contract-based work. And less than 2% of the positions advertised were for internships.

While not entirely surprising, this finding does raise a significant question. If the overwhelming majority of job positions are full-time, how do students or rookies gain experience? Contractor work and internships are often the first step on the career ladder for new professionals, while part-time work can be a great way to study and gain real-work experience at the same time.

One solution to this challenge is to focus on training methods that replicate the work of real data science teams. By learning from industry experts, and building a portfolio based on actual work tasks, you can prove to employers you have the experience needed to take on your first full-time position.

Giedrius also recommends focusing on data analytics first if you do not yet have significant experience. "In many cases, if you want to get into data science but you do not have a relevant degree, it makes sense to go into data analytics," he explains. "You can then move into data science through self study and by looking for opportunities to do data science on top of your data analytics responsibilities."

What salary can I expect?

Overall, data science jobs are well paid, thanks for the strong demand for specialists in this area.

Beyond this general observation, however, it can be difficult to know what an appropriate salary is for the positions you are applying to. Given that you may well be asked what your expected salary is during the application process, this is an important factor to understand.

Data Nerd shares findings on average salary and median salary. While somewhat helpful, the average salary figures are distorted by the industry practice of putting very wide ranges for job postings. In extreme examples, they found job postings where the stated range started at $300,000 and went up to $750,000. 

A more reliable data point is to consider median salary. Here are the median salaries per position according to Data Nerd:

• $126,815 for Data Engineers,

• $120,000 in Data Science,

• and $85,000 in Data Analysis.

When researching for specific positions, use the country filter to get accurate figures for your region.

What skills are employers looking for?

This is the big one in terms of your long-term career development. You can dedicate months of your life to learning a new language or software stack, only to discover employers don't need these competences. So, understanding exactly which skills are in-demand is very valuable.

According to Data Nerd, skills like SQL and Python are more or less universally valued - both feature in the top 3 skills mentioned for Data Engineer, Data Scientist and Data Analyst positions.

For Data Analysts, knowledge of Excel, Tableau and PowerBI is sought after. For Data Scientists, Tableau is also important, along with R, AWS and Spark. 

Here is a full breakdown of the top 10 skills in each role (correct as of June 2023 - Data Nerd's App updates all the time as new listings are posted).

There is one more important point to note before you start learning some of the skills mentioned in this table. "Many of the skills shown in this table are alternatives to each other," Giedrius comments. "So a company will never expect a Data Scientist to know Python, R and Java - they will only be using one of these programming languages. So you definitely don't need to learn all of the skills listed here for a specific role - that would be very counterproductive."

Armed with this data, you now know which skills are required, what salaries you can expect and what kind of training you need. 

So, the next thing you need to understand is how to find the perfect open positions to fit your personality and profile, and land an interview.

Nailing interviews and tech challenges

So, your application stood out from the crowd, and you are through to the next round of the process. Congratulations! Now it is time to sharpen your interview skills and prepare for the tech challenge.

9 handy tips for interviews

Simona's years of experience in recruitment have taught her a lot about what it takes to have a successful interview. Here are her 4 top tips to help you succeed.

What skills should you mention during the interview?

Clearly, you will want the responses you give in your interview to directly answer the questions given. So, while it is always important to prepare, avoid scripting your answers beforehand. You want to be open, listening fully to the recruiter and specifically answering the question they give. 

But instead of scripting, what you can do is targeted positioning. Here's Simona to explain more.

"When you are preparing for a job interview, you should analyse the job position. Do this in such a way that when you go into the interview you know which hard skills and soft skills they are looking for. In other words, you're saying 'OK, the emphasis in the job advertisement is on these X hard skills and these Y soft skills.' So this is what I'll focus on." 

"Prepare yourself to present your answers in a way that covers these points. Of course, you should avoid lying or distorting reality, but I'm talking about targeted positioning which is very important. Your answers do not have to be scripted, but you must think through your overall strategy before the interview. This way, you can give a well-focused answer to a question like: 'What experience and personal qualities do you have that are necessary for this position?'"

This technique will work well for common open-ended questions used in interviews, for example: 

• Which professional skill do you most want to improve, and why?

• Why are you interested in this position?

• Give an example of a time you showed initiative.

• Where do you see yourself in the future?

• What are your biggest strengths?

Interviewers ask these questions because they want to see what your thought-process is like. When you have a wide range of possible points to discuss, seeing which ones you actually choose to focus on can tell them a lot. And if your answers match with the types of hard and soft skills they are looking for, you have a recipe for success.

Tips for success in your tech challenge

When tackling a challenge, here are some helpful tips that can improve your performance.

The wrap up: getting hired in data science and analytics

From this handbook we hope you have gained a strong overall understanding of the types of opportunities available, what employers are looking for, and how to maximise your chances of getting hired.

Let's wrap things up with a couple of overall recommendations.

First of all, Simona stresses that when you are in the thick of a job search, remaining focused and organised is the key to success. "It is important to approach the job search process with a strategic mindset," she says. "Focus on quality over quantity when it comes to reaching out to organisations. By adopting a targeted approach and making a strong first impression, the chances of success in securing the opportunities you want are greatly enhanced."

Secondly, remember that personalising this process is very important. Hopefully, there are lots of useful tips and strategies you can adopt. Nevertheless, remember that each job search will be unique to the individual. Everyone has different priorities and needs, whether we are talking about employers or employees. So, be true to yourself in terms of which jobs you go for and how you present yourself. And remember to do everything you can to understand the other party's needs and tailor your applications and interview technique to suit them.

The opportunities in data science and analytics have never been greater, and there is plenty more growth to come. So focus on your goals, and you can be confident that you will land the right position for you.