Description

Job Summary: We are looking for a Data Science Intern who will help us discover the information hidden in vast amounts of data and help us make smarter decisions to deliver even better products. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our products. Responsibilities: Translate complex findings into compelling narratives. Selecting features, building, and optimizing classifiers using machine learning techniques Data mining using state-of-the-art methods. Extending company’s data with third party sources of information when needed. Enhancing data collection procedures to include information that is relevant for 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 and Qualifications: Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naïve Bayes, SVM, Decision Forests, etc. Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB etc., Excellence in at least one of these is highly desirable. Good communication skills. Experience with data visualization tools, such as D3.js, GGplot, etc. Proficiency in using languages such as Python, C#, JavaScript, Typescript. Experience with NoSQL databases, such as MongoDB, Cassandra. Preferably Azure or AWS experience. Preferably using Windows and Linux OS. Good in applying statistics skills, such as distributions, statistical testing, regression, etc. Good scripting and programming skills. Data-oriented personality.

Role and Responsibilities

• Responsibilities: Translate complex findings into compelling narratives. Selecting features, building, and optimizing classifiers using machine learning techniques Data mining using state-of-the-art methods. Extending company’s data with third party sources of information when needed. Enhancing data collection procedures to include information that is relevant for 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.