Half (49%) of young people fail to consider data science as a career option

Nearly ten years ago, Harvard Business Review famously labelled data science the sexiest job of the 21^st century. However new research from Exasol, the leading high performance analytics database company, suggests data science careers are rapidly falling off young people’s radars — despite data roles ticking a lot of boxes in terms of the skills and activities young people want from a future career, businesses and educators aren’t communicating the importance and application of data in easy to understand terms.  

Exasol’s research of more than 1000 16- to 21-year-olds in the UK (coined D/NATIVES by Exasol because of their everyday digital skills) found that half (49%) don’t consider data science as a career option. When asked about their future plans, 61% of D/NATIVES say they have a clear vision for their career. The most popular skills they want to feature prominently are communicating (39%); decision making (34%); problem solving (33%); finding information (32%); asking questions (30%); telling stories (23%); and maths (20%). All very relevant data science skills. In fact, they all align to five of the top words D/NATIVES used to describe the key characteristics of a data scientist; mathematical, problem solving, analytical, intelligence and confidence.  

Yet, despite clear awareness of the impact data and statistics have on their life, many young people are not familiar with jargon such as data literacy (51%) or big data (50%), demonstrating a clear disconnect between the language D/NATIVES use and the business words used by employers to advertise data careers, leading them to fail to consider the data science field as a career option. 

Commenting on the findings, Peter Jackson, Exasol CDO said,

“Ten years ago, data scientists were in demand thanks to their ability to plug a crucial skills gap and tackle new organisational challenges stemming from the growth of business data. Today, the demand for data scientists and data engineers has more than tripled since 2013.”

The good news is that the respondents to Exasol’s survey possess a lot of relevant soft skills that are crucial in helping organisations realise the full value of their data. Peter Jackson added, “In data teams there is room for all sorts of people, from the technical masterminds to the data storytellers that articulate the meaning of that data and turn them into actionable insights for the business.”

There is work for businesses and educators to do to make young people aware of the context, opportunities and risks surrounding data – demonstrating its power in our everyday lives. How we collect data, how and when we give it away, how it’s used and the potential it has never been more exciting and can no longer be shunned as a career option.

“D/NATIVES have untapped subconscious and habitual data literacy skills ideal for data analysis, storytelling and visualisation of key trends, patterns, and anomalies. Without these future data champions, businesses are in danger of missing out on new ways to solve data challenges today and pushing the boundaries of industry as we know it,” Peter concluded.  

More insights into the attitudes and understanding that young people currently in higher education or just entering the world of work have towards data can be found in Exasol’s report:

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Whether you’re newly entering the workforce, have been recently laid off, are worried about keeping your current job or have been temporarily furloughed and have some time on your hands, there’s no better time to pick up some AI-related skills than right now.

According to LinkedIn, artificial intelligence and machine learning jobs have grown 74% annually over the past four years. Job titles in this category include data scientists and machine learning engineers, but if you’re confused about the differences between a data scientist vs. machine learning engineer, you’re not the only one.

“To begin with, there was no distinction between the two roles,” said Pragyansmita Nayak, chief data scientist at Hitachi Vantara Federal, which provides technology services to federal agencies.

When the two jobs first started growing, companies advertised for data scientists whether the job was more on the data scientist vs. machine learning engineer side.

“That confusion [still] exists today,” Nayak said.

What is your background?

The biggest difference between a data scientist vs. machine learning engineer, experts said, is that they come from very different places.

“Data science has its foundations in statistics and in the business side,” said Justin Richie, data science director at Nerdery, a digital services consultancy.

For example, a data scientist working at a bank might be asked to find out why customers are leaving, he said. The data scientist would decide on what data and analytics are needed and come up with a way to identify customers who are likely to leave.

Machine learning engineers, however, come from the other direction — from software development.

“They’re more focused on the production of the models and embedding them into applications,” Richie said.

In the bank example, a machine learning engineer might take the model created by the data scientist and turn it into production code to embed into a mobile banking application. With that, the insights can become actionable, with the bank taking immediate steps to change the minds of customers looking to jump ship.

Key skills for data scientists

According to Ira Cohen, chief data scientist at Anodot, an autonomous business monitoring platform, “data scientist” is still often used as the overall umbrella term, with machine learning engineer being a narrower subset of it.

But, increasingly, data scientist is becoming a more specialized job category, analyzing business data using machine learning or artificial intelligence, he said. “Similar to the role of business analysts.”

Data scientists often start out as business analysts and boost their math and analytics skills with additional courses or on-the-job training. Some also start out right in data science, with academic backgrounds in statistics or artificial intelligence.

In addition to math and business domain knowledge, data scientists typically need programming skills to be able to develop prototypes of their models. R and Python are the most common programming languages for the job, but Scala, Julia, JavaScript, Swift, Matlab and Go can also be useful. Data scientists should also be familiar with data visualization tools like Power BI, Tableau and Qlik.

Andrew Stevenson, CTO at Lenses.io, a company that offers data platform monitoring technology, once worked on a project with data scientists from an energy trading desk.

“They were able to build the models, test and run locally,” Stevenson said. And then they hit the limit of their expertise, he said. “The models were not production-grade. They had no monitoring, they weren’t version controlled, they were not easily developed in a repeatable way. They were black boxes and if the desktop got rebooted, they had a production incident.”

This is the point where machine learning engineers step in.

“Data scientists are typically mathematical but literate in programming,” Stevenson said. “Data scientists in a financial trading firm — the quants — usually have Ph.D.s in mathematics but are also technically savvy with tooling such as R and Matlab, but they rely on highly skilled, hard-to-find programmers to implement their algorithms and bring them to production.”

Key skills for machine learning engineers

Machine learning engineers typically start out on the software development side and add machine learning skills through on-the-job training or additional study, though some are now graduating from specialized degree programs.

It’s never been easier for a software developer to become a machine learning engineer, said Sachin Gupta, co-founder and CEO at HackerEarth.

“With more and more open source libraries from tech giants like TensorFlow from Google bringing pretrained models for various use cases, it’s becoming simpler for machine learning engineers to experiment with a multitude of models,” he said.

Then the machine learning engineer deploys these models, builds APIs and web interfaces, and builds data pipelines, he said.

Machine learning engineers have some overlap with data scientists in terms of skills. Both may be using R or Python, for example, and both need advanced math skills like linear algebra and statistics.

But machine learning engineers are expected to be more highly skilled when it comes to programming, said Alex Ough, senior architect CTO at Sungard Availability Services. Machine learning engineers also need to know production platforms such as AWS, Azure and GCP and their AI services, he said.

Where the 2 jobs overlap

Larger companies typically see data scientists and machine learning engineers as two separate job functions, Richie said. But at smaller and midsize companies, one person may be doing both jobs, with the company hiring either one or the other. That can be a mistake, he said.

“Hiring a single person to do all of those things is not signing up that person for success,” Richie said.

He suggested that companies that can only afford to hire one person, hire for the specific job they need the most.

“Then cross-train other people at the company,” he said. “That’s what I’ve been advising the customers we work with. For example, business analysts are good for learning data science skills. Use the existing skill sets and hire only the specific niche vertical that you need.”

Working hand in hand

For companies that hire both data scientists and machine learning engineers, the two typically work closely on projects.

“Think of it as a data scientist being the architect of a building,” Nayak said. “And the machine learning engineer is the general contractor who actually builds the building.”

Data scientists start out with the data, the goals and the algorithms, she said, while the machine learning engineer starts with the code. But the two work together on many tasks. Data scientists usually choose the best machine learning algorithm for a particular project, but machine learning engineers have a better idea about the frameworks used by the organization.

“I would talk to the machine learning engineer,” Nayak said. “I would ask what the different options are, what they would recommend.”

Then, after the machine learning engineer has done the development work and put the application into a production environment, the data scientist may be needed again.

“That’s where the data scientist goes back to the end users and works with them and makes sure they are comfortable with the systems,” Nayak said.

How to get the skills

Many online educational platforms are making courses available for free or at a low cost, including those that offer credentials that can be added to a resume. And, to put those skills to some use, there are volunteer opportunities available to write code for nonprofits or help with open source projects.

Even during the pandemic, there are opportunities to network. Many local data science groups and larger conferences and other events are going virtual. Look for opportunities to do presentations about your ongoing projects, find volunteer opportunities and pick up industry gossip about who’s hiring.

Data science is not pandemic proof

At first, it seemed people working in AI and analytics would be spared from the brunt of this crisis. After all, these are jobs that can very easily transition to a work-from-home model.

According to a survey by recruiting firm Burtch Works that was conducted in partnership with the International Institute for Analytics, in the first week of the social distancing in the U.S. 56% of respondents said that they saw no staffing impact as a result of the pandemic. But by the third week, only 40% of respondents could say the same and 38% reported “some or substantial” cuts at their companies.

There’s also been a dramatic decline in LinkedIn data scientist job postings, Burtch Works reported. Listings went from more than 21,000 postings in late March to fewer than 17,000 a month later — though the listings rebounded somewhat to 18,000 job postings by mid-April.

Long term, however, there is significant optimism about this sector, so you may want to consider getting training to become a data scientist or a machine learning engineer or improve the skills you already have.

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Software engineering has blown up to encompass more than 1million employees in the US as of 2018 and is not forecasted to slow in growth. Next to come is the machine learning engineer, who takes an automation or decision making problem and applies cutting edge tools to it.

With the pervasive nature of machine learning (particularly deep learning) across industry, more engineers deploy these tools on a day to day basis. The list of tools that use deep learning that make companies huge profit margins is effectively endless: search recommendation, speech-to-text, voice assistants, facial recognition, advertisements, and more.

How does implementing these models differ from the roles of building vast distributed software-systems? The mindset is similar, but the specializations are different.

Software Engineering —building a data network

The data flow is the key to any at-scale software project. Engineers must choose the right algorithm to deploy on devices locally, what languages to develop in (and what language they compile into), and how many levels in the software stack.

The software engineer ultimately works in the space of language, data structures, and algorithms.

• Language: The development and test languages are the work environment of software engineers. They develop an intimate understanding of capabilities for different languages, and the tradeoffs scale drastically. Python is a favorite because the downstream decisions become so much more fluid (I agree with Python).
• Data structures: Different data structures determine which computer operations are fast — do we want fast access of data (hash table)? Fast post processing with the learning tool (Tensor)? Or something else? Different languages have different properties to leverage, and the best software engineers are fluent in these like a foreign language.
• Algorithms: Standard algorithms are the foundation of technology interviews — sort, search, and so on — because they do matter at scale. “Big O” notation is a quirky tool for learning, but the ideas translate massively when working on deployed systems.

Loving the complexity of one’s own system so that you can create more in it and show off metrics falls short when others try to use it is the downfall of a super-capable engineer. Simplicity is king because it scales and enables collaboration at the company scale.

Good software engineering ultimately will make the task of machine learning easier. The data will be more available and more uniform for distillation into products and value.

Machine Learning Engineering— building a knowledge network

Learning engineers are distilling logged knowledge (data) and creating decision boundaries. The decision boundaries are frequently nonlinear, and frequently difficult to interpret (such as a trading agent or a robot planner), but they are decision boundaries informed by data.

Machine learning engineers think in the space of Models, Deployment, and Impact.

• Models: When should I use a deep model or a Bayesian approximation? Knowing which systems generalize better, can be fine-tuned on-device, and are interpretable is the key for machine learning engineers. Also, the expertise over models is what makes ML PhD’s such valuable hires for technology companies.
• Deployment: Many companies have defined their niche in this area. Device scale artificial intelligence is the current push for consumer electronic companies (ahem, Apple) and model efficiency dominates costs of the digital goliaths. (Facebook, Google, etc). Tesla dominates the automative automation market with unmatched cloud car updates. Next is how individual engineers contribute — more specific models for specific tasks will add up in our lives, and the efficiency of models will change internet speeds and battery life.
• Impact: Ethics. Does the model I am deploying benefit a subgroup at the cost of another? This is something ML engineers need in their repertoire because the dataset you choose and train on will be reflected in your product. Consider if a dataset is collected from a sample of 100 pre-alpha users, how will that translate when it touches millions of unknowing eyes? Data transparency is behind and individuals need to be accountable.

When surveying options for implementation, other machine learners want to be able to extract and mirror useful code in a modular fashion, enabling rapid evolution. I’ve tried to utilize multiple state-of-the-art projects that were trapped in too many layers internally to take the next step and make them impactful at scale in the real world — which is why simplicity is king.

The Theme — Digital is Cheap, Simplicity is King

Both the engineering roles leverage the face that iterating in the digital domain is cheap and fast — every marginal user adds high value at a low cost. With this, the simplest methods tend to dominate because they can be so pervasive — simple methods have better generalization in learning and better interfacing in software.

The best engineering students don’t optimize within the box given to them, they look for cracks that’ll totally change the nature of the game. In software engineering, that is in the form of using new tools and data structures, in machine learning engineering that’ll be in tweaking a new model type or how it is deployed. I suspect as software engineering becomes increasingly automated, machine learning engineers will drive the best companies.

This post was inspired by a conversation on the Artificial Intelligence Podcast, with Lex Friedman hosting Andrew Ng, when discussing the impact that Massive Online Open Courses are having, how computer science is taught, and how the big tech companies dominate markets.

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