5 Skills Needed to Be a Data Scientist

After years of testing top data science talent, the team at QuantHUB have identified 5 key skills that data science testers look for. In this article, we’ll highlight each big data skillset you should develop as a top tier candidate.

LinkedIn recently ranked Data Scientist as the #2 fastest growing job role for 2018, second only to the newly #1 ranked and highly correlated position of Machine Learning Engineer. Glassdoor also confirms the high demand for Data Scientists ranking the role as the #1 job in the United States based on the number of job openings, salary and job satisfaction. These statistics are no surprise to QuantHUB or our clients who experience the need for and the benefits of solid data science capabilities on a daily basis. It is also no surprise to us that with such high demand for Data Scientists to support the growing fields of Big Data, Artificial Intelligence, Analytics and IoT that most reports point to a critical shortage of skills in Data Science.

The most common definition of Data Science as a field is on Wikipedia: “Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.”

What makes it difficult to find Data Scientists is that Data Science is, as Wikipedia says, an “interdisciplinary field”.  Data Science requires a variety of skill sets to do the job. Moreover, the relative importance and interdependency of these skills depends on the data-using nature of a particular company or business strategy that a Data Scientist will support. Some skills are “hard” and some “soft”.  Some skills are interrelated, others aren’t. Given this multi-disciplinary profile, the profession of Data Science still lacks a standard approach to professional development and education that we find in traditional professions such as Accounting.

So with such a wide variety and degree of data-related needs and no consistent definition of the what a Data Scientist does, QuantHUB is tackling the question: What skills should an organization look for to test and develop in individuals aspiring to be Data Scientists in order to ensure success? Based on our experience working on data strategy and management with clients, as a data science tester, developing our own analytical tools, as well as observations in the industry, QuantHUB has determined the following five skills are a core requirement for any Data Science professional.

Programming –  Data Scientists are presented with often very large data sets, or “big data”. Being able to decode big data requires an understanding of programming languages such as Python and database querying languages such as SQL. A Data Scientist need not be an expert programmer, but should be capable of adapting to different programming environments where slicing and dicing data will be required.

Statistics – After slicing and dicing data, a Data Scientist will need to apply statistical analysis to it. Statistics are a core discipline from which data science has evolved. Statistical techniques such as hypothesis testing, regression analysis, and decision trees are basic building blocks of data science. Data Scientists must have the quantitative skills, be able to apply statistics to understand large data sets. They must also be able to apply statistical outputs to a business context in order to provide the foundation for business analytics.

Business Acumen – Somewhere between the “hard” and “soft” skill sets that Data Scientists need lies the bridge that is business skills and business knowledge. Data Scientists need to have a good enough understanding of the business across multiple departments and teams to be able to confer with business professionals, take their abstract business problems and issues, and derive analytical insights. This enables them to assist in developing solutions and strategies that are aligned with business goals.  This is the whole point of Data Science. This skill is particularly important if a Data Scientist is working on the more strategic end of business analytics. The most successful Data Scientists are thus well-integrated within their company, seek feedback from business users and share their knowledge in an iterative fashion.  Additionally, an understanding of business metrics and KPIs that strategists use is helpful.

Communication – A Data Scientist without communication skills is doomed. The whole point of Data Science is to communicate trends and insights to the people who need to use them in their organizational roles. Of critical importance is a Data Scientist’s ability to translate statistical output into actionable recommendations and items and in a way which gives confidence to data-driven decisions and strategies. Data Scientists need to be able to tell the analytical story and its findings in a way that non-technical colleagues can understand and use with determination.  Data visualization, the ability to provide a visual representation of data in a clear, concise manner is also key to good communication. The ability to use modern visualization tools such as Tableau is thus increasingly important.

Problem Solving – Many experts in the field of Data Science suggest that this soft skill is the key differentiator in job interviews and that it separates Data Scientists who test exceptionally from the rest. Great Data Scientists possess a natural intellectual curiosity and desire to answer questions that people have, in effect, a desire to solve problems. They seek to understand what is going on around them. They view using data as one way to accomplish this. This trait is what business people are hoping for in their Data Scientist colleagues. Much of Data Science is about trying to understand the behavior of a complex data set through experimentation and creative approaches, all in the name of problem solving.

The tools, techniques and applications of Data Science continue to evolve. Because the field and its definition are so diverse in nature, so are the backgrounds and skills of the people who fulfill the role of Data Scientists. QuantHUB helps recruiters and corporations vet and test data scientists and related fields to truly gauge their level of expertise. Our comprehensive testing platform covers multiple choice, case study, and in-person interviews to validate analytical talent from Python to R to Tableau.

Visit: www.quanthub.com today to find out how you can begin to test your data science candidates!

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