Selecting features, building and optimizing classifiers using machine learning techniques.
Data mining using state-of-the-art methods.
Enhance data collection procedures to include information that is relevant to building analytic systems.
Processing, cleansing, and verifying the integrity of data used for analysis. Doing ad-hoc analysis and presenting results in a clear manner.
Creating automated anomaly detection systems and constant tracking of its performance.
Skills: Understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests.
Data Science toolkits, such as R, NumPy, Matlab, SQL. Data Visualization tools.
Performance Data Data Visualization Data Collection Data Mining Mining Data Science Algorithms Features Analysis Machine Learning Matlab R SQL Science