Data Science Jobs

Data scientist jobs – they are a sexy new opportunity in 2020. With exciting job prospects, lucrative salaries, and a position at the forefront of technology, who wouldn’t want to be a data scientist? 

Data science is more than crunching numbers. It’s about using data to create solutions. 

And, while AI and machine learning are all the media talks about, the reality of the industry might surprise you. Read on to learn about: 

  • Day-to-day life at data science jobs
  • What large companies are actually looking for 
  • The education you need to get started
  • Income and prospects (spoiler alert: they are both great)
  • How to get entry-level data scientist jobs even with minimal experience (and how to progress your career)

But first, let’s talk about how incredibly important data science is to our society. In the spirit of data analysis, we have the numbers to prove it: 

Fascinating Facts 

We create so much data every day and businesses are yet to take full advantage. A multi-disciplinary approach for taking numbers and turning them into solutions generates more income for the company… 

And more employment opportunities for you!

Here is how the field looks like today:

  • Every day, we generate 2.5 quintillion bytes of data. For just one minute in 2019, users watched 4,500,000 YouTube videos, posted 511,200 tweets, and sent 18,100,000 texts!
  • By 2021, data-driven firms will grow to earn $1.8 trillion annually. 
  • These companies are also 23 times more likely to acquire new customers and six times more likely to keep them. 
  • There is plenty of room for improvement – companies ignore 88% of the data they receive!
  • 96.4% of companies surveyed by New Vantage partners are investing in machine learning and AI (compared to just 68.9% in 2017).
  • The average data scientist salary in 2019 was $96,096. Top employers like Apple pay even more – the average there is a whopping $125,989!
  • Job prospects couldn’t be better – 67.9% of businesses now have a Chief Data Officer (only 12% had appointed one in 2012).
  • Data scientists enjoy high job satisfaction, citing perks like learning opportunities, making a feasible impact on customer experience, and a stimulating work environment. 

Data Scientist Jobs: In a Nutshell

What even is data science? 

Mix data mining (extracting useful information from raw data) with computer science (software development, algorithms, and machine learning) and you get this exciting new field. 

Data scientists turn messy information into pure gold.

They help businesses make sense of the huge amounts of data. Then, they use data to create innovative solutions and products, as well as test them in the real world. Finally, data scientists are the people who make AI and machine learning possible. 

Are data scientists in demand? Absolutely, there is currently a shortage of them.

From data engineers to data science core and the folks working on AI, there are constantly new data science job openings. 

Do data scientists make good money? Yes, the average salary in the US is $96,096! 

They also have very high job satisfaction and they get to make a visible difference for both companies and users. It’s a job where you’re constantly learning new things. Plus, you have a front seat for the AI revolution. Data scientists don’t really get bored. 

How to become a data scientist? There is no single path to success. For your B.S., aim to get math, statistics, and some programming coursework. Python and SQL are indispensable. Higher-level positions, including AI specialists need M.S. or even Ph.D.. 

Where do I find data scientist jobs near me? You can always check our directory (shameless self promotion, wink)!

Ready to get on this rollercoaster? Here is everything you need to know: 

Entry-Level Data Scientist: Getting Jobs

Let’s say you just graduated high school (a.k.a. you’re looking for a dream job) OR you decided to switch careers. You are starting from ground zero. 

How do land a data scientist job

… and why would you even want one? 

First things first, consider a career in data science if you: 

  • … love problem-solving (puzzles, riddles, cryptography – it’s your jam).
  • … have a knack for math and statistics.
  • … are excited about technology (and you know how to work it).

A data scientist takes unstructured data and uses it to make an impact. You will be working directly with product developers and management to solve real-life company problems. 


Data science doesn’t just mean AI and machine learning. Yes, these are exciting new technologies and a lot of companies are investing in them. 

For data scientists, however, it’s not about fancy technology or complicated models. Most of them are actually working on much lower-hanging fruit, which can provide just as much impact. 

It’s a job where you need equal parts analytical thinking and empathy. Math, statistics, and a background in computer science help, of course. However, social skills and a strong drive to reach a solution (to very complex problems) are also a must. 

Data science is a playful field where you get a lot of freedom to be creative. But it’s not about giving your opinion. In the wise words of W. Edwards Deming, a leading thinker in the field of quality: 

“Without data, you’re just another person with an opinion.”

So, first, you learn how to work the data: 

Degree in an Analytical Field

This phrase “a degree in an analytical field” is straight out of a posting for a position at Facebook. Even internships require coursework in a related field. 

What does this mean in practice? 

To apply for jobs in data science, you need proof that you understand statistics, computer science, and math. You don’t need advanced skills in mathematics, though, and this is exactly why leading data scientists hold a variety of degrees. 

Take George Roumeliotis, one of the pioneers in the field. He holds a doctorate in astrophysics!

Start with a B.S. with plenty of data-related coursework. This could be Computer Science, Engineering, Mathematics, Statistics, Physics, or business-related degrees like Economics and Accounting. 

Get Experience Through Internships

To get a job you need experience. To get experience, you have internships. 

If you’re serious about getting a data scientist job, go apply for an internship.

Of course, if you already have some experience, you can skip this step. For complete newbies, however, this is the best way to get your foot in the door. 

Here are the usual requirements for an internship: 

  • Coursework in math, statistics, and computer science
  • Some experience with software development (mainly Python and SQL)
  • Being familiar with machine learning and deep learning models and frameworks 
  • Knowledge of big data technologies, cloud storage, and processing

Are you interested in Machine Learning Jobs?


Since data scientists are in demand right now, a lot of companies will hire straight from their pool of interns. Even if you don’t get offered a job, you’ll gain invaluable experience and get to work with some of the smartest people in the field. It’s a win-win situation either way!

Salary, Benefits, and Working Environment

Entry-level positions can still come with a six-figure salary. This YouTuber started out with $100k per year. This is for a junior data scientist too!

The average pay, according to PayScale is $85,901 but this depends on location and company. For instance, entry-level data scientists get paid 26% more than the average in San Francisco, California. 

It gets better. 90% of all employees have medical benefits, with an increasing number of companies offering generous social packages. 

Day-to-day life in a tech company is pretty exciting too. You will get to work with people at the forefront of technology and innovation – and learn from them. Depending on the size of the company, your role might include only very specific tasks or have a broader scope. 

Say what? 

Let me expand on that: 

Data Scientist Jobs: Your Comprehensive Description

Hal Varian, the chief economist at Google, infamously ranted how the sexy job in the next decade would be statisticians. He supported his theory by noting how the same flavour of sexy job of the 1990s was a computer engineer – something nobody expected.

Then, in 2012, Harvard Business Review called data science “the sexiest job of the 21st century”. Non-tech people were baffled. Maybe they still are. The fact remains, though: 

We live in a world of big data… Big data we’re not fully using or understanding (remember, businesses ignore 88% of the data they have). 

Data scientists take all that information and come up with cool ways to use it.

Large companies have an insatiable appetite for data science pros because of the impact they make. As a data scientist, you have to be equally comfortable with the mathematical and tech aspects, as you are with product development and testing. 

But what will your everyday work life look like? It depends on what kind of data scientist you work as. To help break it down, let’s look at what data science entails: 

Data Science Hierarchy of Needs 

Check out this handy pyramid, developed by the folks at Hackernoon –

Sure, AI and deep learning are cool but first you need to cover basics like data collection and storage. 

As detailed by Monica Rogati’s brilliant article (recommended reading for anybody interested in data science), first you need to ‘know how to count’. This means understanding data collection, database management, exploring and clearing the data. Only then can you choose training data for, say, AI. 

How is this relevant to your future career? 

It tells you what exactly you’d be doing. 

A startup can usually only hire one data scientist. That person is responsible for all levels of data science work – from data collection through learning – all the way to optimization (and AI/machine learning, if the company is investing in that). 

At medium to large companies, labour is divided into data science analytics, data science core, and data engineering:

Four Types of Data Scientist Jobs

Working for a larger company, your everyday tasks can be even more specialized than these three categories. Still, these are the main types of data scientists in the industry right now. 


This is a general job description for this position. Every employer will have different criteria for hiring.

Data Analyst Jobs: Pull and Report Data

Closely related to data engineering (minus the software development), this is the basis of the pyramid. As a data analyst, you’ll be working with SQL databases, compiling and reporting data, and working on data integrity and accuracy. 

Data analysts work closely with data engineers, as well as product development and management. Some of the skills you’ll need to master are SQL, Python, Excel, Tableau, as well as data visualisation. 

These positions are a solid opportunity for newcomers in the field to get hands-on experience and mentoring by more advanced professionals. 

Data Engineers: Taming Big Data

Data engineers (also known as data collection specialists) are responsible for setting up the infrastructure of data collection. They work on basic needs like providing datasets and making sure data flows smoothly through the system. 

Data engineers build software to solve data-oriented problems. In other words, a degree in software engineering is perfect if you want this position. Day-to-day work as a data engineer involves having to: 

  • Develop software to carry out data workflows.
  • Maintain the data collection and data flow software.
  • Take part in the software development lifecycle and create effective approaches to working with data.
  • Collaborate with data scientists, product managers, and other engineers to develop products and solutions.

Data Science Core: Turning Numbers into Solutions

Data science core employees work in the higher tiers of the hierarchy. Their job is more than just crunching the numbers or developing black box models (i.e. an algorithm where you plug in the problem and you get the solution)

Data science core specialists work on analytics – they come up with the metrics to track. Based on that, they develop the features for an AI model (if the company is working on AI) and come up with experimentation frameworks (to see how well a potential product works). 

Data science core works with a wide array of real-world data and comes up with creative ways to derive value from it. 

It’s a job where you need a tech core, but also empathy and creativity. 

Say you’re looking for the region where people spend the most time watching videos on your platform. Thailand pops out. So, should the company invest more in the region? Or is there another reason why Thai people spend so much time on the videos – i.e. longer buffering times? (example from this interview with Facebook data scientist and YouTuber Joma Tech)

This is the kind of challenge a data science core specialist faces on a day-to-day basis. 

Data Science Analytics: AI & Machine Learning 

Finally, data science analytics is where all the sexy machine learning and AI work happens. These are the highest-paid folks and they are also the ones with the most advanced skills and the most experience. An M.S. or even a Ph.D. are required here. 

Data science analytics specialists don’t have to work on data collection or even A/B testing. Instead, they can just focus on implementation of AI technologies. For this, however, you need an in-depth understanding of all the other levels of the data science hierarchy. 

There are subdivisions in machine learning jobs, too. From machine learning architect to business intelligence developer, there is plenty of work for creative, tech-savvy enthusiasts. 


Machine learning engineer = software engineer that specializes in machine learning 

Data science analytics specialist = data scientist working on machine learning implementations 

Some people working in data science analytics are machine learning engineers, some aren’t.

But here’s the kicker: 

You don’t need AI for everything.

Even large companies like Netflix have lower-hanging fruit to work on. You can have a huge impact on customer experience (and company earnings/success) without deep learning/AI.

For many data science newbies, working on AI becomes the end goal. The reality of the industry, however, is that we need much more data science core specialists. 

It’s not about how fancy and complicated the technology you’re working on is. Data science is an applied field. The only thing that matters is the impact of your work (which can be huge).  

This isn’t to discourage you. You can eventually get to the level where you work on AI and deep learning. 

The point is that all data science jobs can give you opportunities to make a difference!

Data Science Jobs: Salary Breakdown

Mandatory disclaimer: salary depends on education, experience, location, and your negotiation skills. That said, here is everything you need to know about potential earnings before you apply or start negotiating your salary. Knowing what you’re worth means you will not sell yourself short!

Data Analyst Earnings 

On average, data analysts make $59,986 per year or $28.85 per hour. Total pay (including salary, bonuses, profit sharing, etc.) ranges between $39,205 and $85,357. 

Amazon data scientist jobs come with an average salary of $77,613 per year. The pay is highest in San Francisco, New York City, and Washington. The key skills that will increase your pay are: 

  • Python
  • Tableau Software
  • SQL
  • Data Modeling
  • Data Mining
  • Statistical Analysis

Less tech-dense jobs like data entry pay 16% less on average. 

Data Engineer Salary 

The average salary for a data engineer is $91,452 per year or $40.41 per hour. 

This is nearly double what data analysts make! Even the lower-paid data engineers make over $25 per hour. 

Total pay for data engineers ranges between $64,806 and $140,560. High-paying employers include Facebook (where the average salary is $122,465) and Amazon ($108,743 per year). 

Again, data engineers in San Francisco make the most (up to 35% above average). The skills that relate to higher pay include: 

  • Apache Spark
  • Apache Hadoop 
  • Amazon Web Services
  • Big Data Analytics

SQL and data mining aren’t typically what data engineers spend their time on. Generalists or those who work in startups might still need these skills but they correlate to lower pay. 

This is not to say if you learn SQL, you will make less! What this stat tells you is you need more than SQL to make it as a data engineer. 

If you are looking for a SQL Job: Visit Leftronic.

Data Science Core Earnings

The average data scientist salary is $96,096/year or $35.51 per hour. 

Say what? How come the average is higher for data engineers? 

Because ‘data scientist’ is an umbrella term that can include everyone from data analysts to machine learning engineers. 

The vast majority of job postings for data science core pay over $100,00 per year. This is a starting salary, often for an entry-level data scientist

Again, the pay varies by state. For instance, the entry-level data scientist salary in NYC is 4% above the national average. If you’re a pro at machine learning, your starting salary in NYC can be as high as $92,610. In San Francisco, it can reach $110,593! 

Data scientist jobs in Chicago, on the other hand, pay 9% less than the average for the country. 

Data Science Analytics and Machine Learning

The average entry-level machine learning engineer salary is $111,217 per year or $36.63 per hour. Your total pay will also include an average bonus of $10,072 and profit sharing of $10,241. This means the total pay for machine learning engineers adds up to $72,542 – $166,387 per year!

A senior data scientist salary can go up to $160k with an average of $126,295. Their total pay ranges between $97,000 and $176,000. Top-paying companies like Microsoft offer an average of $149,835 per year (before bonuses and profit sharing). 

While there is a difference between data science analytics and machine learning engineer, skills in deep learning can boost your pay by up to 9%. 

The Highest Paying States/Cities

A couple of places stand out, whether you’re a data analyst or you’re doing deep learning and AI development. 

The average data scientist salary in Silicon Valley (San Francisco Bay area) is 26% higher than the national average. This is no surprise – this is where high-paying companies like Facebook, Amazon, and Microsoft are. 

While your data scientist salary might be the highest in California, you should also consider taxes and the cost of living. 

Data science jobs in NYC also pay 4% above the average, but this is a notoriously expensive city to work in. The combination of low tax and cheaper living makes the data scientist jobs in, say, Houston, Texas (which pay around the national average) a better option for many. 

Remote Data Science Jobs

As with any tech job, you can do data science as a freelancer or teleworker. 

Bitcoin exchange Kraken, data management giant Q-Centrix, and genetics innovator Invitae are just a few of the companies that hire remote workers.

For most of those remote jobs, you will still have regular business hours. Some employers allow you to work from home only if you’re in the US. Bali beaches and party life don’t mix well with that…

Ultimately, most data science jobs are office positions with standard working hours. The position of a data scientist is equal parts tech and collaboration. Being physically present in the office doesn’t just make you more productive, it also makes communication easier and yielding better results. 

The highest-paying and most challenging jobs in data science are not remote. 

Data Science: Is That A Degree? 

Short answer:

Yes, but only M.S. and Ph.D. programs.  

Long answer:

Most data science jobs don’t require more than a Bachelor’s Degree. To get into master’s and Ph.D. programs, as well as internships and jobs, you need a B.S. with coursework related to data science. 

So what are the best degrees for a career in data science? 

Computer Science 

The three most important aspects of data science are math, statistics, and programming. Computer science programs, especially those with a strong mathematical foundation, can give you all of these. If you’re currently a computer science major, make sure you have some math coursework, especially calculus, statistics, and probability. 


There is no field more versatile than mathematics. Math gives you strong analytical skills, which you can then supplement with courses in programming to round off your data science. 


Any engineering program will give you math superpowers. Software engineering will also provide the programming skill set for data science. Engineers enjoy employability straight out of university and they have the advantage of having pursued an applied science. 

Any Other Analytical Degree

Data scientist job postings usually ask for an analytical degree. Essentially, any B.S. with coursework in mathematics, statistics, and computer science works here. Even if you haven’t taken programming in college, you can teach yourself Python and SQL, which are the two essential languages for data scientists.  

Self-Study Resources and Certifications

Starting with some books: 


  • “Python for Data Analysis”, by Wes McKinney is the best resource for a data scientist already familiar with Python. It teaches you to use the language for data crunching specifically.
  • “Python Machine Learning”, by Sebastian Raschka is a beginner-friendly resource on the fundamentals of machine learning, with Python code included. 
  • “Hands-On Machine Learning”, by Aurélien Géron offers in-depth insight into the machine learning landscape, covering a vast amount of information. It also features two production-ready Python frameworks—Scikit-Learn and TensorFlow, to help you learn by doing. 


Towards Data Science is a great blog to keep you updated with best practices and updates in the field. Here are the certifications they recommend for aspiring data scientists: 

  • Professional Certificate in Data Science by HarvardX
  • Analytics: Essential Tools and Methods by Georgia TechX
  • Applied Data Science with Python Specialization by the University of Michigan (Coursera)

N.B. These aren’t requirements to apply for a job. Employers are only interested in your analytical and problem-solving skills (and how you use them to create impactful solutions). These resources are purely for self-study, a.k.a. building these skills. 

Speaking of skills, let’s go through the checklist: 

Technical Skills for Data Science

The three basics of data science are: 

  1. Math 
  2. Statistics
  3. Programming

The practical skills you need for success include: 


  • Coding – the most common language is Python, Java, Perl, and C/C++ can provide useful, too. 


  • Data visualisation – it’s a must for data analysis positions and a useful skill, even if you’re doing AI and machine learning. 


  • SQL database and coding – since SQL is specifically designed for managing data, all prospective data scientists should understand it… And yes, this is still the case, even if you work with Hadoop/NoSQL
  • Hadoop – most data science positions require at least a basic understanding of Hadoop. The big data storage and processing framework has almost endless applications in the field. 
  • Spark – this is Hadoop’s faster competitor. It’s also a big data computation framework but it caches its computation – meaning you can learn complicated algorithms at record speeds. 
  • R programming – this is not a must for an entry-level data scientist but it will make you stand out from other candidates and open doors to more senior positions. 
  • Machine learning and AI – while not all data scientists work with AI (remember, it’s not about fancy algorithms but real-life solutions instead), understanding machine learning techniques will give you access to jobs at the forefront of innovation. 


These will all equip you to work with unstructured data and turn it into impactful solutions. 

Soft Skills Matter 

Data science is an applied field. You need empathy, social skills, and the motivation to create products that will make a difference. Here are the main soft skills to harness for data science career success: 


Even if you’re the only data scientist in a startup, you would still need to collaborate with other colleagues. The job will have you working closely with product managers, as well as engineers and data analysts. Clear communication, professionalism, and compromise are the key qualities of a superstar data scientist. 


Crunching numbers is only the base of the pyramid, remember? 

Data scientists are hired for their innovative solutions based on real-life data. 

For instance, when LinkedIn was first starting out, data science expert Jonathan Goldman suggested a ‘people you may know’ feature. He wasn’t just one guy with a cool idea – the idea itself came from his analysis of user data. 

Ultimately, Goldman’s idea gave LinkedIn the strong social aspect it has today. This translated into better revenue for the company, sure, but arguably also improved user experience. 

Modern data scientists need creativity no less than Salvador Dali or Picasso did. The only difference is that data science entails basing your creativity on real-life data. 

Lifelong Learning

Data science is an up-and-coming field. As technology develops, being a lifelong learner is the only way to stay relevant. Jobs in data science are perfect for people with endless curiosity. One thing is for sure – you will never be bored! 

Data Scientist Jobs: The Bottom Line 

Once upon a time, statistics sounded like the world’s most boring field. 

Combine it with smart algorithms, state-of-art technology, and the chance to make a genuine difference in people’s lives… Suddenly it sounds a whole lot cooler, right? 

Whether you’re just getting into the field or you’re looking for a new professional challenge, the sexiest job of the 21st century (according to Harvard Business Review!) will not disappoint.

Good luck and make sure you check our directory to find tech jobs in your area! 

Data Science Jobs: FAQs

Here are the answers to everything you need to know! 

Q: What Does a Data Scientist Do?

A: Data scientists take large, unstructured sets of data and use them to create products and solutions. Some data science specialists work on collection and analysis (though this is usually handled by data engineers and data analysts). The vast majority use data to make an impact, whether this is through smart new algorithms or deep learning. 

Q: What Skills Are Needed to Be a Data Scientist?

A: A strong background in either math or statistics is immensely helpful. Any analytical degree can help you get that – whether it’s mathematics, physics, computer engineering, or even something like accounting. 

On top of the mathematical skills, you need some technical skills – Python, SQL and working with databases, as well as R programming for more senior positions. 

AI and machine learning are an exciting aspect of data science, but they aren’t a requirement for entry-level or even mid-level data scientist jobs. There are plenty of awesome things you can do without AI (but knowing the fundamentals will help you stand out). 

Soft skills such as communication, collaboration, and creativity are also invaluable. This is an ever-evolving field so endless intellectual curiosity, too. 

Q: Is There a Demand for Data Scientists?

A: Yes, absolutely. Businesses are starving for rockstar data scientists. Using data in novel, unexpected, and impactful ways benefits both the company and the end customer. 

In fact, data-driven firms are 23 times more likely to attract new clients and by 2021, they will boost their profits by a healthy $1.8 trillion!

Safe to say that a data scientist is a huge asset to any company. 

Q: How Do I Get a Job in Data Science?

A: Start with a B.S. in an analytical field. The exact bachelor’s doesn’t matter as much as the coursework itself. Make sure you have some background in the following: 

  • Statistics and probability 
  • Mathematics (calculus, linear algebra)
  • Programming and IT (coding with Python and being fluent in SQL)

Further your skills through self-study – the Professional Certificate in Data Science by HarvardX is just one of the high-quality online courses available. 

Apply for an internship to get hands-on experience and mentorship from actual people in the field. 

Consider getting a M.S. or even Ph.D. if you want to work in cutting-edge fields like AI and deep learning. 

Q: What Can I Do With a Data Science Degree?

A: Data science is not a degree you can get at the B.S. level, but M.S. and Ph.D. programs will open doors to jobs at the forefront of the tech revolution. You can work on Artificial Intelligence and machine learning, as well as go into academia. With this advanced education, virtually all data scientist jobs will be an option for you.