What It Means to Be a Data Scientist Today

By Yao-Nan Chen, Machine Learning Scientist, Appier

Unless you have been hibernating under a rock for a few years now, you already know that explosive growth in the volume of available data is disrupting business as we know it. This data can be a goldmine for businesses that know how to capture, analyze and use it to power artificial intelligence (AI) technology. And that’s where data science and my role come in.

IBM has predicted that demand for data scientists will increase by 28 percent by 2020. The Harvard Business Review, way back in 2012, said that being a data scientist is the sexiest job of the 21st century.

I have been working in data science since 2013 and I still come into work at Appier each day eager to solve new problems.

What Data Scientists Actually Do

Simply put, data science involves using data to generate solutions that solve practical, real-world problems. In the business world, examples revolve around AI-powered solutions, such as pushing recommendations for users based on their demographic or usage pattern, or analyzing why sales of a particular product is dropping.

Data scientists set out on solving such problems by first extracting and consolidating data, which we then analyze for patterns and trends. We use this to build predictive models, derive insights, and implement proof of concepts to test the proposed solution to the problem at hand. The problems that we work on are very specific and often have no one standard solution. Hence, data scientists are tasked with thinking out of the box to come up with a variety of possible solutions.

The impact of our solutions is known only when they are implemented; so often, if the solution fails to meet the desired outcome, we have to go back to the drawing board and start over. But this just adds to the challenge and the excitement of trying to pin down that elusive solution and make it work.

What Makes for a Good Data Scientist

Of course, every job has some less lovable bits and the burden of the data scientist is data cleaning! In most cases, the data we gather is ‘dirty’, with errors and discrepancies in it. For example, data showing that sales of a product have dropped dramatically may simply mean that malfunctioning machines have failed to capture the data accurately.

Most data scientists will agree that data cleaning is the most boring part of this job. Our inside joke is that data science is 80 percent cleaning of data and 20 percent complaining about it!

But jokes aside, data cleaning is painstaking but important work. If not done right, it can have a huge impact on the accuracy and reliability of insights.

Aside from this kind of assiduity and attention to detail, a good data scientist, no matter how good they are technically, must also have a thorough understanding of business domain and the organization’s business goals. Our solutions have to be creative, but also useful and practical.

Keeping Up with the Latest Research

In this context, keeping up with the latest research in the area of machine learning can help us stay on top of trends and monitor breakthroughs to specific problems. We don’t need to reinvent the wheel – if a particular problem has been solved before, we can always work off that.

I regularly read papers on advances in machine learning, as well as in the specific domains that I am interested in.

It’s equally important to engage in discussions with peers, keep track of their recent research and poll their opinions on machine learning trends. This will help you keep abreast of all that is happening in this area.

Growing Demand for AI Expertise

Unfortunately, there is a gap between the growing demand for data scientists and the supply of talent in the area. AI is a new track and there is a shortage of people with the required expertise. What widens the gap is that not every data scientist is a good business person. They may be stellar at solving problems in an academic or research-based environment, but often fall short when it comes to real-world business problems.

Data scientists today must constantly evolve in terms of skill set. As the adoption of AI and deep learning grows, we are automating lower-level tasks and moving onto more complex problems. We already have some mature tools that can be used to build simple models for many business cases, and these are becoming simpler to use.

In the near future, data scientists will be required to know how to leverage and use problem-specific information. As AI becomes more complex, data scientists will need to work on more abstract problems and leave simple processing and analyses to automation software.


About the author:

Yao-Nan Chen is Machine Learning Scientist at Appier. He has more than five years of experience in Machine Learning, Data Science and Data Engineering and three years of experience in practical E-commerce recommendation system. Prior to joining Appier, he worked at Yahoo Taiwan on E-commerce Recommendation System, App notification recommendation system, Model tuning for sales volume prediction, etc.

 

 

How Businesses Can Get AI Head Start With AlaaS

While data-driven artificial intelligence (AI) often dominates business and technology headlines these days, the coverage generally centers on the development of the technology. Yet for all the celebration around AI, the main concern for enterprises is still how they can get started with the technology, in order to generate actionable insights from their data.

Large technology-oriented organizations tend to build their own in-house data science team, which might not be the best approach for smaller companies with less resource or technical expertise. This is where AI-as-a-Service (AIaaS) could just be your alternative passage to AI-powered business.

In-House Data Science Team Is Not for Everyone

Although data science is one of the most in-demand professions worldwide, companies are facing a massive talent gap: There are more positions to fill than there are qualified data scientists available to fill them. Quality data scientists require a wide range of skills, which sometimes could vary depending on the nature of data to be analyzed and the scale and scope of work.

Apart from finding the right talent with the right expertise, recruiting them usually involves high salary, additional benefits, perks and a host of other incentives, as you might be competing with some of the best brands around the world.

Even if you invest in building a data science team, some of the roles may end up becoming obsolete. As AI continues to advance, data scientists are required to evolve as well. There will be an increasing trend towards both machine learning engineers and deep learning scientists, who can program AI to self-learn, which may set off another arm’s race for top talent.

Given the above challenges, companies can consider AIaaS as a fast lane to AI adoption for business.

AI off the Shelf

AI-as-a-Service is similar to the Software-as-a-Service (SaaS) from which its name derives. Businesses turn to SaaS providers for web-based software. In much the same way, enterprises can leverage AIaaS providers’ off-the-shelf AI tools and services for various needs, such as optimizing once-inefficient processes and saving on operating costs.

While adopting AI technology through AIaaS can help streamline your operation, it also allows you to focus on other core business functions, such as product development, marketing and sales, to make a significant impact on your business goals. For example, AI-powered customer segmentation and prediction enables you to understand customers more intimately, in order to offer a more engaging user experience, drive conversion and minimize customer churn.

“Although AI is being used to predict customer behavior to some extent, it will get a boost in the future. Businesses will use AI to detect if a customer is willing to purchase the product, seeking support or switching to another provider even before they actually approach,” said Liam Martin, Co-founder of Time Doctor, in The Next Web.

Estée Lauder, for example, wanted to raise brand awareness among young women online and drive mailing list sign-ups across all screens. It leveraged Appier’s CrossX AI technology to nurture and build a high value audience pool, effectively boosting cross-screen conversion by 300% to 1100%.

There are other benefits that companies turning to different AIaaS providers for their AI needs may experience, such as achieving insights and breakthroughs in data that humans may have overlooked, improving customer service through chatbots that are available around the clock, or helping protect their customer data through the use of smart contracts.

Finding the Right AIaaS Partner

Before you start a hunt for an AIaaS partner, you first need to identify the most valuable problem in your business that can be improved with AI, and what specific outcome you are trying to achieve.

With an increasing number of AIaaS providers available in the market, there are always basic questions to ask before you deciding on one, such as how they will harness your data or work with your own IT team. In addition, to ensure a smoother start, you also want to see whether the graphic user interface of the provider’s platform is easy to understand and navigate.

Look for evidence of the provider’s track record in the space. Successful AIaaS providers should be able to present you with case studies, research, reports, industry recognition and testimonials. When evaluating these materials, you will be able to see if they have successfully addressed the needs of enterprises with problems similar or even identical to your own.

Once you find your trusted AIaaS provider and go through their onboarding and integration process, you are set. You can begin optimizing, understanding, analyzing and predicting with precision and scale like never before.