1. What are the responsibilities of Data Scientist II?
Collects, analyzes, and interprets large data sets to identify trends, patterns, and provide key business insights. Performs data mining, cleaning, and aggregation processes to prepare data, implement data models, conduct analysis, and develop databases. Develops insights and reports from multiple structured and unstructured data sources using programming, statistical, and analytical techniques and tools. Maintains continuous collaboration with teams to understand the underlying purpose, focus, and objective of each data analysis project to ensure alignment and support. Designs, develops, and implements the most valuable data-driven solutions for the organization. May require a master's degree in computer science, mathematics, engineering. Typically reports to a manager. Occasionally directed in several aspects of the work. Gaining exposure to some of the complex tasks within the job function. Typically requires 2-4 years of related experience.
2. What are the skills of Data Scientist II
Specify the abilities and skills that a person needs in order to carry out the specified job duties.
Each competency has five to ten behavioral assertions that can be observed,
each with a corresponding performance level (from one to five) that is required for a particular job.
1.)
Analysis:
Analysis is the process of considering something carefully or using statistical methods in order to understand it or explain it.
2.)
Computer Science:
Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to practical disciplines.
3.)
Big Data:
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed to big data are veracity (i.e., how much noise is in the data) and value. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on." Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.
