Is a career as a data scientist in your future? The door is wide open to post-grads who want to enter this new realm where you can be an invaluable strategy consultant in any number of industries. As Big Data comes of age, the demand for qualified scientists is exploding. Current forecasts indicate a projected shortage of 190,000 data scientists in the U.S. by 2018.
The Role of a Data Scientist
The work of a data scientist is rooted in business operations, from which an analytics plan is created. This plan defines how often massive volumes of data will be utilized to answer business questions. Once the plan is in place, data scientists begin their analytical work, using a range of tools and methodologies. They are involved in:
- Intellectual property/security: Data scientists manage this responsibility as their company solidifies its advantage over external competitors.
- Productization of data projects: This occurs when there is market value for the output of data efforts and work can be resold to customers.
- Tactical optimization: Improvement of marketing campaigns and other business processes.
- Predictive analytics: Anticipation of future business demands, events and developments.
- Nuanced learning: Driving such business strengths as development of a deeper understanding of customer behavior.
- Recommended engines: Management of entities such as Amazon product and Netflix movie recommendations.
- Automated decision engines: Responsibility for engines ranging from automated fraud detectors to self-driving cars.
- Communication with corporate management: Data scientists are called upon not only to analyze data, but also to communicate its value and implications to other members of the C-level team.
The Skill Set of a Data Scientist
While data scientists play a multi-faceted role, their strength is based on three main competencies:
- Mathematical experience: Solutions to business problems often involve analytic models that are deeply grounded in math theory. And the ability to understand how models work is just as important as knowing how to build them. Data science is more than just statistics. Many inferential techniques and machine learning algorithms require a strong knowledge of linear algebra. Processes like SVD – used for dimension reduction/latent variable discovery – are grounded in matrix mathematics.
- Technology and hacking: This is not “hacking” as in breaking into computers. An effective data scientist has the creativity and ingenuity to use technical skills to develop solutions for pressing business problems. They must leverage technology to wrangle enormous data sets and work with complex algorithms. This requires sophisticated tools such as SQL, SAS and R, all of which rely on technical/coding ability. A true “hacker” is a technical expert who can use problem-solving abilities to master data exploration.
- Business acumen: A data scientist is first and foremost a business strategist. Data teams are invaluable to their employers because they can learn from data in ways no one else can. They are superbly positioned to add substantial business value based on this capability.
The importance of data science is based on a simple but profoundly important premise: It enables companies to strategize and operate more intelligently and thus, more successfully. It’s all about adding substantial enterprise value by learning from data.
The expert team at Select Group, Inc. can partner with you as you advance your data science career. Contact us today!