Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

What It Means to Be a Data Scientist Today

Author | 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.

WE ARE HERE TO HELP

Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.

YOU MIGHT ALSO LIKE

What Is AI as a Service and How Can It Help Your Business?

The global market of artificial intelligence (AI) continues to grow. According to IDC, at least 90 percent of new enterprise apps will embed AI by 2025. One of the reasons behind this trend is a growing awareness of the benefits of AI, together with the increasing accessibility of advanced AI capabilities, enabled by AI as a service (AIaaS).   What Is AI as a Service? AI as a service is like any other out-of-the-box offering. It is artificial intelligence software being offered by a third-party provider to a client as a service, including a wide range of different AI-powered capabilities. These capabilities hosted by the third party sit in the cloud and are available to the end-users over the internet, making AI more accessible. Demand for AIaaS is growing as businesses increasingly see the value it is creating for competitors. Industry figures suggest that the global market of AI as a service will hit US$10.88 billion by 2023, up from US$1.13 billion in 2017. However, it could potentially be much higher as it becomes more available.     What Are the Different Types of AIaaS? There are various types of AIaaS solutions. Which type you choose comes down to your business

What Is a Recommendation Engine and How Does It Work?

Recommendation engines are everywhere these days. In fact, some of the biggest brands we engage with every day are built around one, including Netflix, Amazon, Google, and Goodreads. Thirty-five percent of purchases on Amazon come from product recommendations. So, what is a recommendation engine, and how does it work?    What Is a Recommendation Engine? A recommendation engine is a type of data filtering tool using machine learning algorithms to recommend the most relevant items to a particular user or customer. It operates on the principle of finding patterns in consumer behavior data, which can be collected implicitly or explicitly. Netflix uses a recommendation engine to present viewers with movie and show suggestions. Amazon, on the other hand, uses a recommendation engine to present customers with product recommendations. While each uses one for slightly different purposes, both have the same goal: to drive sales, boost engagement and retention, and deliver more personalized customer experiences. In the past, recommendations would come from a salesperson or friends and family. Today, we have passed this task in the hands, or minds, of algorithms. As a marketing tool, you could say that these machines are well-trained in the art of up-selling and cross-selling.  

Don’t Stop at the Checkout: How to Nail Post-Purchase Customer Engagement

You have made a sale, with your marketing efforts succeeding in converting a casual browser into a paying customer. Congratulations! Now it is time to relax and enjoy the fruits of your labor, right? Not quite. Customer engagement should not stop after the checkout. Instead, focus on how to retain your existing customers and increase their loyalty to your brand. These are both vital for your company’s survival as 40 percent of the average e-commerce store’s revenues come from repeat business. Here are five ways to get your post-purchase engagement right.   1. Shipping and Delivery E-commerce can sometimes seem a little impersonal. There are no shop assistants to ask (a bot isn’t quite the same), no store to walk around, no products to see and feel in person until the order is delivered. So, the shipping and delivery is your chance to make a personal connection with your customers. Of course it should be as quick and efficient as possible, and you should keep your customers up to date on when their package(s) will arrive. Moreover, you can add a little extra touch to make them feel that you value their custom. Fashion retailer Boden sometimes adds extras like