Once dubbed as the sexiest job of the 21st century by The Harvard Business Review, data scientists take pride in having adept technical skills in providing solutions to problems through data visualization, pattern recognition, text analytics, and data preparation among many other skills.
Given the various industries that utilize data and draw valuable insights from it to enhance their businesses and services, data scientists play a huge role in the progress of any business.
In fact, data scientists can be found in at least eight fields: statistics, mathematics, data engineering, machine learning, business, software engineering, visualization, and spatial data.
If you’re a student of data science and analytics, you won’t have to worry about employment opportunities after you graduate. Listed below are six types of jobs for data scientists:
1. Data analyst
Perhaps the most common job, data analysts are similar to data scientists in such a way that they both use data for solving problems. While data analyst may be unable to develop new systems or algorithms, they focus their job on collecting, processing, and performing statistical data analyses through statistical tools and techniques.
Once they have relevant information, they begin data interpretation by spotting trends, correlations, and patterns in complex data sets to provide for improvements.
2. Business Analyst
If data scientists concern themselves in improving business processes such output, distribution, and productivity, then they can be fit for business analyst positions.
They are responsible for devising data-driven solutions to organizational problems, and inventing new systems to improve efficiency in personnel, product offerings, and service processes.
3. Marketing Analyst
Data scientists who find themselves interested in market conditions, consumer behavior, and product studies may appreciate landing jobs as market research analysts. They often guide companies in identifying which products or services will sell, to which specific market type, and at what price.
Market research analysts are responsible for gathering relevant data on consumer demographics, their buying behavior, and competitors’ profiles. They are also in charge of assessing the company’s marketing strategies to determine whether it works for them or not. Often, their job outputs impact on how a company designs, sells, and distribute their products and services.
4. Data Architect
Data scientists can be architects that develop and maintain data management systems.
They work on creating plans for integration, centralization, protection, and maintenance of a company’s internal and external data sources. Data architects’ jobs are significant by providing easy access to pertinent and significant information for everyone in the company.
Their responsibilities cover database structures, inventories, data acquisition, security, and recovery.
5. Quantitative Analyst
With the objective of reducing risk and increasing profit generation for a business, quantitative analysts make use of financial data to guide management decisions in investments, pricing, and risk management.
“Quants,” as they are also called, examines market trends and dynamics, trading strategies and system performance, and investment management to enable them in testing new models, products, and programs to ensure their company won’t be at risk of financial loss.
6. Statistician
When data scientists get themselves involved in the application of statistical theories and methods for data gathering, analysis, and interpretation, they likely fall under the job category of statisticians.
Oftentimes, statisticians tackle market research for quality control and product development for businesses, while they can also work in the academe for heavier statistical applications.
Statisticians are responsible for data extraction through existing models or new ones, whichever is required by the situation. They also employ statistical tools and algorithms such as SAS and SPSS when analyzing and interpreting data, while simultaneously recognizing trends, relationships, and patterns among their data sets.
Now that you’ve read through them, which of these types do you see yourself taking?
