Data scientists are in high demand. Across a range of industries, organizations are searching for highly skilled data scientists (aka data analysts) who can make sense of the rapidly rising volume, variety and velocity of data available today. Those data scientists are tasked with producing new insights that can help improve customer relationships, enhance efficiencies, identify new market opportunities and more
Compensation can be impressive, often in the six figures. Organizations recognize that in today’s data-centric world, data scientists can play essential roles in generating revenues.
What are the qualifications for the job? To be a successful data scientist requires much more than a degree in math or statistics.
1. Scientific approach
Given the number of job openings in data science and the potential for a high salary, it’s not surprising that many programmers, application developers and engineers are interested in making a career transition to data science. But for many, becoming a data scientist will demand a new, more scientific approach to solving problems than the linear approach of engineers. The most highly prized data scientists know how to generate hypotheses, run experiments and modify hypotheses based on experimental results. While some prospective data scientists might require a shift in thinking, these requirements open the door to career transitions from more scientifically inclined disciplines.
2. Business perspective
Data scientists must also understand how to integrate scientific thinking with a business perspective. The right applicants for data scientist jobs will be those who understand how to ask the questions that the business wants answered, and then translate results into insights that are directly relevant for business goals.
3. Readiness to collaborate with IT
To manage, analyze and deliver insights from big data, data scientists need to work closely with IT. Beyond administering the infrastructure needed to support data science, IT groups often have deep expertise and experience with data management — and effective, efficient data management is crucial for successful data science. The right data scientist applicant will have a background in collaborating with IT along with a willingness to help bridge the gap between IT and the business.
4. Multilingual programming skills
For now, data scientists often need substantial programming skills, preferably in a variety of languages. An increasing availability of automated tools in the future might minimize the requirement for software development expertise, but today, experience with Java, R, Python and SQL will make certain data scientists stand out in the applicant pool.
5. Understanding of privacy issues
The process of analyzing and acting on big data can introduce significant privacy issues. For example, how do you use information from pharmacy purchases and social media posts to create targeted promotions for healthcare products while fully complying with Health Insurance Portability and Accountability Act (HIPAA) regulations? Your organization will need to develop clear guidelines for newly hired data scientists, encourage them to take personal responsibility for what they do with customer data, and give them tools that are designed with privacy in mind. But the best potential data scientists already understand the importance of maintaining privacy while mining sensitive information.
As long as data continues to flow in torrents, organizations will need well-qualified data scientists to find patterns, identify trends and produce insights that lead the business forward. Organizations that combine skilled data scientists with tools for facilitating analysis and delivering intelligence across the enterprise will have a strong competitive edge.
Data science as it relates to true big data (i.e., exploring petabytes of less structured information) is a young, immature field and largely a frontier of unknowns and opportunity by comparison. Learn more about Data Science here: mindmajix.com/data-science-training