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Hi all

I work with some machine learning methods in my job, in semiconductor field: usually we start from a data set, more o less big, we clean it through some criteria and algorithms and then run some algorithms, already available, to get a model of these data. Some times we get some good results many other not, So I'm wondering, what kind of job is a data scientist? what are his expertise, what are his knowledge of data he's working on, what are his goals inside the company? Should they develop (writing codes) specific methods for different work area?

thanks 

Rosario

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There are all sorts of data scientists.

  • Some are BI analysts, and rarely code (they even use GUI's to access databases, so they don't even write SQL queries - the tool does that for them; however they must understand database schema.) But they are the guys that define metrics and work with management to identify data sources, or to create data. They also work on designing data dashboards / visualizations with various end-users in mind, ranging from security, finance, sales, marketing, to executives..
  • Data engineers get the requirements from these BI analysts to set up the data pipelines, and have the data flow throughout the company and outside, with little pieces (usually summarized data)  ending up on various employee laptops for analysis or reporting. They work with sys admins to set up data access, customized for each user. They are familiar with data warehousing, the different types of cloud infrastructure (internal, external, hybrid), and about how to optimize data transfers and storage, balancing speed with cost and security. They are very familiar with how the Internet works, as well as with data integration and standardization. They are good at programming and deploying systems that are designed by the third type of data scientists, described below.
  • Machine learning data scientists design and monitor predictive and scoring systems, have an advanced degree, are experts in all types of data (big, small, real time, unstructured etc.) They perform a lot of algorithm design, testing, fine-tuning, and maintenance. They know how to select/compare tools and vendors, and how to decide between home-made machine learning, or tools (vendor or open source.) They usually develop prototypes or proof of concepts, that eventually get implemented in production mode by data engineers. Their programming languages of choice are Python and R.
  • 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. They sometimes use more advanced statistical modeling techniques.

Depending on the size of the company, these roles can overlap. Many times, an employee is given a job title that does not match what she is doing (typically, "data scientist" for a job that is actually "data analyst".)

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