● Understanding theoretical concepts of statistics/probability, data mining, machine learning.
● Understanding how these theoretical concepts could be applied to real-world problems.
● Knowledge and hands-on experience with Python or other relevant programming languages.
● Knowledge and hands-on experience with any recommender engine tools or frameworks.
● Knowledge and hands-on experience with any visualization tools or frameworks.
● Understanding the developing process of data science projects: CRISP-DM or others.
Skills and abilities:
● Strong English verbal and written communication skills.
● Ability to work independently with limited supervision.
● Track record in data analysis, data science, machine learning.
● Relevant levels of theoretical knowledge in statistics/probability, data mining, and machine learning.
● Collecting, transforming, and preprocessing raw data to prepare it for analysis.
● Deriving descriptive statistics out of the preprocessed data.
● Building statistical and probabilistic models.
● Creating visualizations and analytics reports in the form of dashboards.
● Designing, developing, training, and testing data mining, statistical, probabilistic models, and algorithms.
● Providing comparative research on different algorithms and models.
● Implementing the model in a form that can be easily used by engineers, documenting its interfaces.
● Delivering the model in a form that can be easily deployable and maintained.
Based on collected data and product usage statistics, candidates are able to create models that affect the improvement of the product metrics. Exploratory data analysis, building recommender engines, statistical models, etc.