Data Scientist
Data Visualization & Deployment :
- Reporting the results in Tableau, shiny, D3, ggplot2 based dashboards
- Deploying the R, Python based applications in Shiny Server, cloud infrastructure (AWS, Azure)
Education: Bachelor degree in CS or Engineering
Predictive Models development:
- Perform feature engineering using SMAC method to determine important features in the dataset to perform predictive modelling
- Analyze the data and design the machine learning models preliminarily in Rapidminer, Azure ML, Rattle, WEKA
- Perform supervised machine learning techniques – linear regression, decision trees, random forests, neural network, support vector machines using e1071, rpart, nnet, caret, glmnet, rpart, scikit-learn, milk
- Implement unsupervised learning techniques – k-means clustering, hierarchical clustering, Hidden Markov models using kmeans, hclust, scikit-learn, pattern
- Developing front end user interfaces, dashboards using Django, shiny, tableau