Skill Requirement:
Programming such as R, Python and Pyspark
Query Language such as Sql, Hive, Pig
One or More Visualization Tools such as Tableau, Power Bi, Qlikview
Sound Knowledge of Applied Statistics Distribution, Regressions, Statistical Testing/ Hypothesis Testing.
Analytics Models such as Time Series, Regression, Clustering, Classification Models
Ml/ai Techniques such as Decision Tree, Random Forest, Knn, Svm, Neural Networks
Nlp Techniques for Text Data Analysis, Image to Text Processing
Exposure to Etl Tools, Rpa, Blockchain and Splunk Will Be An Added Advantage
Lead and Guide in the Data Science Team.
Integrate External or 3rd Party Data with Internal Data to Make Meaningful Use of It in Data Science.
Conduct Data Profiling, Data Cleaning, Data Processing Using Traditional Etls or Pyspark in Traditional Database or Big Data Systems
Review the Design and Implementation of Data Cleaning and Processing and Verifying the Integrity of Data Used for Analysis
Creating Automated Anomaly Detection Systems and Constant Tracking of Its Performance
Select Features, Build and Optimize Classifiers Using Machine Learning Techniques
Build Analytics Models to Answer Business Questions