Also referred to as big data wranglers, data scientists play an important role in helping companies achieve business success. As such, this field continues to be a promising career path. With advances in technology, however, the current role of data scientists may change in the years ahead. It will be important for data scientists to continue to hone current capabilities while developing new skill sets that can adapt accordingly to meet organizational needs.  


The current role of data scientists


As a professional operating at the intersection of business and information technology, a data scientist possesses the capability to gather large amounts of data to then analyze and synthesize into actionable plans. Although they may vary depending upon skill level and company needs, the typical responsibilities of a data scientist often include the following:

  • Conducting undirected research and framing open-ended industry questions.
  • Extracting huge volumes of data from multiple internal and external sources.
  • Employing sophisticated analytics programs, machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling.
  • Thoroughly cleaning and pruning data to discard irrelevant information.
  • Exploring and examining data from a variety of angles to determine hidden weaknesses, trends and/or opportunities.
  • Devising data-driven solutions to the most pressing challenges.
  • Inventing algorithms to solve problems and building tools to automate work.
  • Communicating predictions and findings to management and IT departments through effective data visualizations and reports.
  • Recommending cost-effective changes to existing procedures and strategies.


“Successful data scientists have a strong technical background, but the best data scientists also have great intuition about data,” said Lisa Qian, data scientist at Airbnb. “[They] are also great at communicating, both to other data scientists and non-technical people.”


How a data scientist is different


While the description of a data scientist may seem similar to the role of a data analyst, they are different. The two professions may share some of the same responsibilities but not all. Consider the following list of typical responsibilities of the data analyst, in comparison to those of the data scientist: 

 

  • Working with IT teams, management and/or data scientists to determine organizational goals.
  • Mining data from primary and secondary sources.
  • Cleaning and pruning data to discard irrelevant information.
  • Analyzing and interpreting results using standard statistical tools and techniques.
  • Pinpointing trends, correlations and patterns in complicated data sets.
  • Identifying new opportunities for process improvement.
  • Providing concise data reports and clear data visualizations for management.
  • Designing, creating and maintaining relational databases and data systems.
  • Triaging code problems and data-related issues.


What type of person is best suited for this role? Data analyst Al Melchior says curiosity and creativity are essential, among other characteristics: “Being able to convey your findings—whether it’s to an audience of readers or a small team of executives making business decisions—is also a key to success, and that’s where the creativity comes in.”


What the future may hold


With big data integration essential to business success, the role of the data scientist has become more important than ever. However, some experts predict that as the field of technology evolves, the job title itself may no longer exist. In an article for Forbes, big data expert Noah Gift had this to say: “From where I stand, the end result will be that yes, data science as a degree and as a capability are here to stay, but the job title is not.” 


Because of technological advances like artificial intelligence (AI) and machine learning, Gift says the role of data scientists will change, but the skills they bring to the table will still be in high demand. That is, if individuals continue to adapt abilities that cannot be automated, including:


  • Communication skills
  • Applied domain expertise
  • Creating revenue and business value


“The only thing that is certain is change, and there are changes coming to data science. One way to be on top of this trend is to not only invest in data science and machine learning skills but to also embrace soft skills,” Gift said.

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