The DNA of the Modern Data Scientist

The extreme proliferation of data over the past decade has shot the role of the data scientist to the limelight. While the real beginnings of statistical modeling can be a debatable topic, what remains unflinching true is that the role of the data scientist has evolved from being a number cruncher to one who enables businesses to thrive in a market that is marked by competitive frenzy. Today, data scientists are no less than knights weaving wonders tackling enormous masses of messy and complex data points and using their formidable skills to clean and organize them and then apply their analytical prowess to uncover hidden solutions to complex business problems.

I like how Greg Boyd, director Protiviti succinctly describes the growing role of the data scientist. He says, “As organizations begin to fully capitalize on the use of their internal data assets and examine the integration of hundreds of third-party data sources, the role of the data scientist will continue to expand in relevance.”

In this data age, data scientists are the rising stars of the business and have well-deservedly come out of the IT back rooms. But when it comes to being a modern data scientist, is being a statistical pro enough? Here’s a look at what I feel should be in the DNA of the modern data scientist

Statistician and more

Being an ace at playing with numbers is a skill that data scientists have to be proficient in. Studies reveal that most data scientists are highly educated with a Masters or a Ph.D. degree under their belt. However, in 2018, the data scientist has to not only be a statistician but also be proficient in Machine Learning, Statistical Modelling, Bayesian Inference, Logistic Regression, and other Supervised Learning areas such as Decision Trees and Random Forests, Clustering, and Experiment Design amongst other. This is because they have to employ sophisticated analytics programs, machine learning, and statistical methods to prep the data for use in Predictive and Prescriptive Modelling.

Programming Pro

Data Scientists have to work consistently to explore and examine data from several angles to derive insights that matter. They also have to design new algorithms for problem-solving and develop new tools to automate work. A good knowledge of programming languages comes in handy here. Knowing a statistical programming language like R and Python and knowledge of database query languages such as SQL becomes important. Along with this, knowledge of Multi-variable Calculus & Linear Algebra also come in handy for algorithm optimization especially when they want to build in-house implementations. Anand Rao, global artificial intelligence, and innovation lead for data and analytics at consulting firm PwC says, “To be really successful as a data scientist, the programming skills need to comprise both computational aspects — dealing with large volumes of data, working with real-time data, cloud computing, unstructured data, as well as statistical aspects — [and] working with statistical models like regression, optimization, clustering, decision trees, random forests, etc.”.

Critical Thinker

Data Scientists are problem solvers, and, hence, the ability to be a critical thinker separates a good data scientist from a great one. Data Scientists of today have to have the capability to understand business problems, markets and decisions to be able to abstract what contributes to solving a problem as opposed to things that can be ignored. Along with this, they have to have the skills to assess one problem from multiple viewpoints and directions.

Data Curiosity

What inferences should you make from the data in hand? What truths can be revealed? How does the data speak? The best data scientist is motivated by the curiosity to explore the mountain of data at their disposal and want to explore it in creative ways. These scientists are more interested in asking questions and are brimming with curiosity.

Domain Expertise meets Business Acumen

Data Scientists don’t just have to have the knowledge of quantitative and technical aspects to transform businesses. Business acumen and domain expertise are important traits to find in a data scientist. Why? Because it is these data scientists who have the capability to create value by acting as business consultants and by being the ones who enable intelligent strategy development. Knowledge of business workflows, strong understanding of the industry and the business problems an organization is aiming to solve are skills inculcate.

Communicator Par Excellence

Communication is vital to a data scientist’s role. After all, their job does not end with exploring and playing with the data. It becomes the responsibility of the data scientist to fluently communicate his/her technical discoveries to their non-technical teams, sales, and marketing, and even the CEO.

They have to deliver actionable and quantified insights in a manner that can be understood by the non-technical population. Since the results propounded by the data scientists will be employed for directional action, it becomes imperative that the data scientist has the capability to make the stakeholders appreciate what is being presented to them - this includes the problem, the data at hand, the success criteria, and the result to make sure everything is in context using strong storytelling skills.

Risk Analysis

A data scientist is, after all, a scientist. His focus, thus, has to be in making process improvements that deliver business benefits. They need to be proficient in concepts of business risk analysis to be able to build in risk analysis at the inception of data modeling and thereby; mitigate the risks that the business is trying to solve.

Finding a great data scientist is no easy job. While it can be easy to find mathematical and statistical geniuses, organizations have to make sure that this technical knowledge is complemented with the intellectual horsepower, great intuitive skills and supreme confidence in the models they build. Someone who has these qualities is bound to be a successful data scientist.

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