Top 10 Unique Data Science Jobs That Are Trending in 2021
by Analytics Insight
October 4, 2021
The emergence of artificial intelligence has spurred the adoption of data science. This technology has become an invaluable asset to industries around the world. With massive amounts of data produced daily, customers expect businesses to handle this data with care; therefore, there has been an increase in employment for data science professionals. Businesses take advantage of this data by extracting meaningful insights to make more data-driven decisions.
Here are the 10 best data science jobs for tech aspirants that are hot in 2021.
â¢ Analytical translator: Becoming a good analytical translator requires technical knowledge and some business understanding. An analytical translator can appropriately prioritize machine learning initiatives based on business goals. In this role, candidates are required to build relationships with people from other companies and manage relevant projects.
â¢ Data architect: Data architects are responsible for articulating new data collections, ensuring accurate data quality, eliminating data redundancy, and working to create the best architecture design for business workflows intelligence and analysis.
â¢ Data scientist: Data scientists play an essential role in business configurations. They understand the market barriers and provide the best solutions through data analysis and processing. Candidates should have computer, statistical and math skills as they analyze, process and model data to achieve business goals.
â¢ Data engineer: Data engineers work in a variety of settings to create systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret their next course of action to achieve goals organisation. They create data pipelines and cloud data integrations, solve complex data problems, and solve data plumbing issues.
â¢ Data Analyst: The primary responsibility of a data analyst is to understand the needs of investment researchers, analyze various data resources for possible inconsistencies, execute data analysis projects and assignments, and create reports for data collection.
â¢ Software engineer-data platform: Candidates are expected to work on building resilient and thoroughly tested distributed systems. They work hand in hand with machine learning engineers to understand the inputs and outputs provided by the models. Software engineers need to have a deeper understanding of different programming languages ââand robust software development applications.
â¢ Machine learning engineer: Machine learning engineers should have detailed knowledge of data science and software engineering. They need to master model deployment, monitor metrics, resolve pipeline integration, and ensure the scalability and flexibility of the deployment environment.
â¢ Python back-end developer: Developers work on the design of software solutions and ensure that the solutions provided meet the constraints of architectural guidelines. They also ensure that automation guidelines are followed accurately and guide Scrum team members on design topics. They also collaborate with various teams and analyze their software needs.
â¢ Business intelligence analyst: Business Intelligence analysts turn data into information to increase business profits. Using analysis, visualization and data processing, BI analysts identify market trends to help managers and business leaders understand potential business threats and increase profits.
â¢ Statistician: Statisticians apply statistical methods and models to real world problems. They collect and interpret data to help companies in their decision-making processes. They also design data collection processes and communicate with stakeholders to advise them on organizational and business strategies.
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