If you are a Data Scientist wondering what companies could have the most career opportunities or an employer looking to hire the best data science talent but aren’t sure what titles to use in your job listings — a recent report using Diffbot’s Knowledge Graph could hold some answers for you.

According to Glassdoor, a Data Scientist is a person who “utilizes their analytical, statistical, and programming skills to collect, analyze, and interpret large data sets. They then use this information to develop data-driven solutions to difficult business challenges. Data Scientists commonly have a bachelor’s degree in statistics, math, computer science, or economics. Data Scientists have a wide range of technical competencies including: statistics and machine learning, coding languages, databases, machine learning, and reporting technologies.”

DATA SCIENCE COMPANIES: IBM tops the list of employers

Of all the top tech companies, it is no surprise that IBM has the largest Data Science workforce. Amazon and Microsoft have similar amounts of Data Science employees. Despite their popularity, Google and Apple are in the bottom two. Why is this the case? It could have something to do with their attitude to how to attract and retain a data scientist. The report does not clearly mention the reasons for these rankings. 

However, Data Scientists want to work for companies that provide them with the right challenges, the right tools, the right level of empowerment, and the right training and development. When these four come together harmoniously, it provides the right space for Data Scientists to thrive and excel at their jobs in their companies.

TOP FIVE COUNTRIES WITH DATA SCIENCE PROFESSIONALS: USA, India, UK, France, Canada

The United States contains more people with data science job titles than any other country. Glassdoor actually names “Data Scientist as the best job in the United States for 2019.”  After the United States are the following countries in this order:

• India
• United Kingdom
• France
• Canada
• Australia
• Germany
• Netherlands
• Italy
• Spain
• China

China has the least amount of data science job titles at 1,829 compared to the United States’ number of 152, 608. But what is the scenario for Data Scientists in Europe? What is the demand and supply? 

Key findings indicate that demand for Data Scientists far outweighs supply in Europe. The existence of a combination of established corporations and up-and-coming startups have given Data Scientists many great options to choose where they want to work. 

MOST SOUGHT AFTER DATA SCIENCE JOB ROLES: Data Scientist, Data Engineer and Database Administrator.

Among all companies, the most common job roles are Data Scientist, Data Engineer and Database Administrator. Data Scientist is the most common job role among all companies, with Database Administrator coming in at second place. If you remove Database Administrator, you find that Microsoft leads the way in terms of data science employees. This means that the reason for IBM’s lead in its data science workforce could largely be due to its sheer amount of Database Administrators. Unsurprisingly, across every job title in data science, males outnumber females 3:1 or more.  It is also interesting to note that this ratio only exists within the Database Administrator category. At the Data Scientist category, the ratio reads 6:1.

It also comes to no surprise that Data Scientist ranks number 1 in LinkedIn’s Top 10. It has a job score of 4.7, job satisfaction rating of 4.3 with 6,510 open positions paying a median base salary of $108,000 in the U.S. However, it is important to note that these positions do not work in isolation. A move towards Data Science collaboration is increasing the need for Data Scientists who can work alone and in a team as well. By utilizing the strengths of all the different job roles mentioned above, data science projects in companies remain manageable and their goals become more attainable. The main takeaway is that despite the vast amount of job titles, each role brings its own unique expertise to the table. 

DATA COLLECTION AND ANALYSIS

Diffbot is an AI startup whose Knowledge Graph automatically and instantly extracts structured data from any website. After rendering every web page and browser, it interprets them based on formatting, content, and web page type. With its record linking technology, Diffbot found the people currently employed in the data science industry at a point in time to provide an accurate representation of the statistics mentioned in this article. 

As a relatively new role, “data guru” is a challenging job specification to draft for. Organisations are seeking highly-skilled and well-educated individuals to fulfil the position but, the truth is, the data scientist an organisation needs is not a guru, but a colleague.

Most organisations forget that recruiting the right talent is just as much about them as it is about the potential candidates. For example, does the organisation provide an interesting and successful environment for the data scientist to thrive in? Does it create new opportunities and positions for data scientists? Does it support its data scientists and allow them the freedom to work creatively?  

Understanding what data scientists look for is crucial when looking to recruit and retain the right data talent.

So, what makes a data scientist tick?

The fact of the matter is that the attrition rate for data scientists is very high. A recent poll by KDNuggets data scientists revealed that more than one in three expect to stay in their job for three years of less. There are a number of reasons that can lead to a data scientist deciding to hand in their notice, and often these things are in the organisation’s control – the company’s culture and technology available for the data scientists to use.

If the organisation doesn’t provide access to data and the tools necessary for data scientists to do their jobs well, it will lead to frustration. More importantly, these barriers make it difficult for data scientists to achieve their goals and perform to their best level, which understandably results in shorter tenures.  

Moreover, from a cultural perspective, many businesses aren’t quite up to speed with data. This starts with the C-suite: if senior management cannot see the value of a data-driven culture, then it will stifle efforts. A data scientist will soon feel under-appreciated and question the point of their analyses and recommendations if action isn’t being taken by the business.

Even if data is at the heart of the business, the data scientist is often left out of the decision-making process. Not only does this dissociate them from the hard work they have done, but it often leads to their work being misinterpreted, with the full benefits of the analyses being lost on the board.

What will draw a data scientist to work for a business?

1.The right challenge

Data scientists are often drawn to innovation – they want to be a part of it, to evoke it, and to drive it. First and foremost, you will attract data talent by ensuring that your organisation is pushing the boundaries of data analytics and use. Nothing is more engaging than a challenge, and data scientists want to be challenged by your company if they’re going to consider it as a place to work.

2. The right tools

This almost goes without saying. A good comparison is surgeons. You wouldn’t expect a heart surgeon to be able to carry out their job properly or effectively if they didn’t have the right tools or equipment available to them in the operating room. It’s the same for data scientists. Without the right tools in place, data professionals may only be working with partial, fragmented datasets or they may not have access to all the data they need, in order to gain the insights that will help to transform the business.

3. The right level of empowerment

With the right tools in place, people need to be given the space, time and trust to think and work creatively. Taking on-board their insights and actioning their suggestions will go a long way in making a data scientist feel appreciated and included in the company’s success.

4. The right training and development

Innovation is a constant within data analytics – from new tools and developments to learning from others’ methods and implementations. It is important your data scientists are continuously challenged and are learning new skills to keep up with this ever-developing market. Your organisation should open up a dialogue with your data professionals, so that you know what they want, what they are good at, and what they need from you. Only then can you help them develop themselves and grow into an integral role for the business.

Conclusion – It takes two to data science

The hiring process is not a one-way affair – while the organisations must make the decision to hire a data scientist based on their skills and experience, the data scientist must also decide whether the organisation is the right place for them to grow and develop their career.

As soon as organisations start realising this, then they can work on becoming a more attractive and exciting business to work for – providing the right challenges, tools, culture and environment for data scientists to thrive. In doing so, the pool of prospective data professionals that are applying to work for the business will inevitably increase, enabling them to hire the best people and to help the business grow and maintain data science success moving forwards.

TOP DATACONOMY ARTICLES

Introduction to Bayes Theorem With Python

“So I feel like there is not a lot of good information out their on how to use Bayes Theorem for modeling – especially with Python code. Like try figuring out how to understand a Bayesian Linear Regression from just Google searches – not super easy. So I thought I would maybe do a series of posts working up to Bayesian Linear regression.”

How Flink Became an Apache Top-Level Project

“A multi-coloured squirrel may not seem like the most obvious choice of logo for a data processing technology; then again, the team behind Apache Flink have hardly done things by the book. As Flink grew in Dataconomy’s back yard in Berlin, we had the pleasure of meeting some of the data Artisans, and discussing with CEO Kostas Tzoumas how Flink transformed from a research project to a Top-Level Project.”

Criteo’s Prediction on Hadoop: How and Why it Came About

“At Criteo we display online advertisement, and we sell clicks to our clients. But it has not always been like this. Actually, the shape of our prediction engine –and its underlying architecture- had to evolve as our business grew.”

TOP DATACONOMY NEWS

A Neural Network That Can Outsmart Wine Snobs at Their Own Game

What does a machine learning scientist who also happens to be a wine enthusiast come up with after a chance encounter with a legendary Bordeaux wine? An application that might be able to predict the quality of wine.

Creators of Siri Snap Up 12.5m Dollars for Viv- An AI That Can Teach Itself

Viv Labs, a stealthy startup founded by the team that created Siri, has secured 12.5 million dollars in Series B funding, in order to carry out its vision of an advanced AI. If reports are to be believed, the round was led by the Silicon Valley’s elite Iconiq Capital, taking the total investment into the firm so far, to $22.5 million.

White House Appoints First US Chief Data Scientist

“In a first, the White House has appointed a certain Dr. D.J. Patil as the Deputy Chief Technology Officer for Data Policy and Chief Data Scientist, it was announced Wednesday. As part of the CTO team, DJ will work closely with colleagues across government, including the Chief Information Officer and U.S. Digital Service.”

TOP EVENTS

What You Missed at Big Data Berlin 4.0

The fourth Big Data Berlin took place on Thursday at Instituto Cervantes with insightful talks by four industry professionals and plenty of time to network before and after over beer and pizza kindly provided by Criteo.

10th March, 2015- Actionable Behaviour Analytics For Web and Mobile, London

“A Great Opportunity to Network and Learn about Behavioural Analytics at the Barclay’s Escalator (Mile End, London) about creating the ultimate behavioural funnel (RSVP here).”

TOP DATACONOMY JOBS

Data Scientist for BIG   

“At Bonial International Group, we want to manage data as a company asset; focus on quality is at the top of our list, which of course results from rational, well-informed decision making. Our teams need the most insightful and accurate information they can get, and it will be your task to help them drive our business forward with precision and predictability.”

Leidenschaftliche Programmierer und Machine Learning Experten for Freiheit.com

Wir gehören seit 1999 zu den Pionieren in der Entwicklung großer Internet-Systeme. Wir haben nur Manager, die selbst auch Programmierer sind. Wir sind erfolgreich, im Markt etabliert und extrem gut organisiert. Wir haben trotzdem unseren Gründercharme nicht verloren.

Solutions Architect- Mark Logic   

“MarkLogic is seeking a Solutions Architect to be an integral part of the DACH team preferably based in Munich. The successful candidate for the role will be a highly seasoned technologist, who has successfully led and delivered multiple large-scale technical projects in complex environments.”