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A Data Scientist is a skilled professional who uses modern tools and techniques to develop solutions for companies based on the business challenges and opportunities available. With the use of statistical methods, data visualization techniques and machine learning algorithms, they can build predictive models and solve complex problems.
Data Scientists derive meaningful information from messy and unstructured data. They also communicate important information and insights to business leaders and various stakeholders.
Simply put, a data analyst makes sense out of existing data, whereas a data scientist works on new ways of capturing and analyzing data to be used by the analysts.
A Data Scientist develops algorithms and data models customized to their unique industry based on where the business would like to improve efficiency, service and brand reach.
A Data Scientist automates tedious tasks and generates insights using machine learning models.
A Data Scientist uses predictive analytics to better understand customer behaviour and preferences, ad performance, and other customer-facing metrics so the business can generate more revenue.
A Data Scientist creates suitable and personalized A/B testing frameworks. They use this to compare the performance of different features, such as logo designs. These frameworks reduce any type of bias or statistical errors and maximize the value of the test outcome.
A Data Scientist analyzes new data sources. They try to understand how the new data source fits into preexisting customer and statistical models and how it affects business analytics.
Expertise in all phases of data science, from initial data discovery through data cleansing and model selection, validation and deployment;
Knowledge and understanding of common data warehouse and data lake structures;
Experience with using statistical approaches to solve analytics problems;
Proficiency in popular machine learning frameworks;
Familiarity with common data science and machine learning techniques, such as decision trees, K-nearest neighbours, naive Bayes classifiers, random forests and support vector machines;
Experience with techniques for both qualitative and quantitative analysis;
The ability to identify new opportunities to apply machine learning and data mining tools to business processes to improve their efficiency and effectiveness;
Experience with public cloud platforms and services;
Familiarity with a wide variety of data sources, including databases and big data platforms, as well as public or private APIs and standard data formats, like JSON, YAML and XML;
The ability to aggregate data from disparate sources and prepare it for analysis;
Experience with data visualization tools, such as Tableau and Power BI;
The ability to design and implement reporting dashboards that can track key business metrics and provide actionable insights; and
The ability to do ad hoc analysis and present the results in a clear manner.
