A data scientist\u2019s job might seem too generic \u2014 but in reality, there are different kinds of data scientists in the workplace. Not every data scientist works in a similar way or scale. Here is a guide for you to check the different \u2018kinds\u2019 of data scientists you\u2019ll meet in any organisation.\nTypes Of Data Scientists\n1.Machine learning experts: This category of data scientist is involved with plenty of tasks like algorithm design and testing. Their job is mainly to design and monitor predictive and scoring systems. They usually develop prototypes or proof of concepts, that eventually get implemented in production mode by data engineers. They develop algorithms that suggest products, pricing strategies and demand forecasting. Their work may further help in achieving better inventory management and strengthening supply chain networks of the organisation.\n\n2.Statisticians: This class of data scientists often are comfortable handling large datasets and build models of various kinds, from distribution models to significance testing, to machine learning and deep learning. \n\n3.Actuarial Scientist: Actuarial scientists are in high demand today. The exams, which are about 13 in number, have to be cleared to become an actuarial scientist. These are very competitive to qualify and end up becoming a successful actuarial scientist. Banks and financial institutions rely a lot on actuarial science to predict the market conditions and determine the future income and profits and losses. It requires a very strong background of mathematics, apart from being a statistics-pro. The category demands the professionals to apply mathematical and statistical models to BFSI (Banking, Financial Services and Insurance) and other associated professions. \n\n4.Analysers: These are the data scientists who spend a lot of time analysing the data. Someone skilful in statistics mathematics and data manipulations fits the best for this role. Data analysts are junior data scientists doing a lot of number crunching, data cleaning, and working on one-time analyses and usually short-term projects. They interact with and support BI or ML data scientists. \n\n5.Platform Builder: Data science platform contains all the tools required for executing the lifecycle of the data science project spanning across different phases. A data science platform helps data scientists enhance their analysis by helping them run, track, reproduce, share and deploy analytical models faster. Usually, all these tasks require a lot of engineering effort but a data science platform gives you the extra tools to speed up analysis and helps in leveraging analytics effectively. Platform builders build platforms and code for the purpose of collecting data. Platform Builders are more likely to work in distributed systems, like Hadoop.\n\n6.Data Preparer: Data Preparers engage their work more with SQL and less with machine learning. Their job is to update and maintain information on computer systems and archives. It's an important role as information in these systems is only valuable if it is accurate, up to date and useable.\n\n7.Software programming analysts: Unlike traditional coders, this class of professionals are involved with number crunching through programming. Needless to mention, they are a master at logical thinking. They typically deal with big data every day to automate tasks and reduce computing time, using their programming skills. They are also required to handle database and associated ETL tools to extract data, transform it by applying business logic and to load it into appropriate data visualisations.\n\n8.Quality Analyst: These data scientists use new analytic tools to prepare visualisations that help in decision making across various teams in the organisation. Also known as localisation analysts or test analysts, they test programs, games and any software to make sure they are reliable, fully functional and user-friendly before they are released. They use a test plan to inspect thousands of lines of code to make sure they are flawless. They report flaws and weaknesses in the program. They may also fix any system problems or glitches and make suggestions to make a software program work better. \n\n9.Data Evangelist: This type of data scientists spends a good portion of her time engaging with others. They act like decision makers and work with the product development team. They are needed to ensure required reports and data updates are available.\n\n10.Spatial engineers: Required in organisations that need spatial data science and spatial statistics, spatial engineers are involved with testing the software and creating automated tests to dig potential problems. They generally need to work with GIS, DBMS, Data Analytics, and Big Data Systems, and the related open source software like QGIS, PostgreSQL, PostGIS, R, and Hadoop tool. Spatial is considered as a core infrastructure of the modern IT world, which is substantiated by business transactions of major IT companies.\nWho Should You Hire\nNo matter what kind of a data science you plan to hire, he\/she should be able to adapt to your company skillset. In fact, one of the most important things is that he\/she should be able to integrate his\/her work with the other members of your team. They should be able to work with a team and understand the problem effectively to function profitably.