A data scientist is a business analyst who has a strong foundation math, statistics and computer science. When makes data scientists unique is their powerful business acumen and ability to concisely communicate findings to leaders that influences how organizations approach and solve business problems.
Retail Data Scientist
A data scientist who works in retail settings will be responsible for providing marketing and customer insights for business stakeholders, according to Harvard Business Review. They accomplish this through synthesizing business knowledge and applying statistical, predictive modeling, data mining and customer profiling techniques. Retail data scientists are expected to be creative problem-solvers who are motivated to develop new approaches to optimizing business process through quantitative methods and cutting-edge technologies. They perform large-scale statistical research and modeling projects. They then communicate their recommendations to increase customer retention, marketing optimization and sales modeling. They design optimization algorithms, deploy new analytical tools and leverage large-scale data sources and complex data sets.
Health Care Data Scientist
Health care data scientists work directly with teams of health care professionals that include data analysts, coding specialists and IT administrators and informatics technicians. Health care data scientists develop analytical and statistical algorithm solutions that predict health care consumer and industry trends. They drive process and data collection automation into health care processes, IT platforms and care practices. Health care data scientists work closely with health informatics teams to review designs, establish best practices and audit medical coding for compliance. They acquire data from external sources, such as national databases, and also internal databases to evaluate efficiency, improve quality and innovate solutions.
Homeland Security Data Scientist
A homeland security data scientist will apply their math and statistics expertise to accomplish initiatives and reach agency goals related to cyber-security, digital intrusions and network exploitations and vulnerabilities. They analyze digital problems to determine technical solutions and then propose new conceptual designs to increase cyber-security efficiency. They analyze security data using statistical and mathematical methods, and then apply lines of reasoning and computation methods to solve existing problems. They also identify new security products, technologies and architectures that will prevent security breaches and operational problems. Homeland security data scientists must have relevant experience involving data mining, science and programming. They must have strong computational algorithms creation and information retrieval skills.
Required Qualifications
At minimum, an entry-level data scientist must have a bachelor’s degree in science, technology, engineering or mathematics (STEM) field. They must be trained in machine learning, statistical analysis, systems design, artificial intelligence and software engineering. Most data scientists have a masters or doctoral degree in statistics, applied math, information science or relevant quantitative discipline. Data scientists should have experience designing experiments, drawing conclusions and making recommendations. To do this, they must know how to extract, manipulate and analyze data through large scale data analysis projects. They must be fluent in advanced statistical and machine learning techniques such as resampling methods, decision trees and dimension reductions.
Related Resource: Software Test Analyst
In addition to the above mentioned competencies, data scientists must be fluent in a statistical programming language, such as Python, and a database querying language, such as SQL. In order to predict performances and optimize algorithms, data scientists must have a strong knowledge of linear algebra and multivariable calculus. Finally, data scientists must know how to visually present complex information in non-technical ways.