Job description

We are looking for a mid-level data scientist who can build statistical tools to discover the information hidden in vast amounts of data and help projects managers make smarter decisions to deliver even better profits in her projects. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with AI in risk management products. Requirements Your Responsibilities as a Data Scientist Selecting features, building, and optimizing classifiers using graph-based machine learning techniques, specifically node2vec, and node embeddings Data mining using state-of-the-art graph computing methods. Extending / Enhancing projects 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 Understand statistical roots of Maximum Likelihood Estimation (MLE) which also is a widely used technique in machine learning with time series, panel data and discrete data. Skills And Qualifications Excellent understanding of machine learning techniques and graph algorithms, such as Breadth-first search, Topological sorting, Graph colouring, Katz-Centrality, Decision Forests, etc. Experience with common data science toolkits, such as Python, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable. Great communication skills Experience with data visualization tools, such as D3.js, GGplot, etc. Proficiency in using query languages such as SQL, Hive, Pig Experience with NoSQL databases, such as MongoDB, Cassandra, HBase Good, applied statistics skills, such as distributions, data moments, statistical testing, regression, etc. Good scripting and programming skills Data-oriented personality A bachelors degree, or MSc degree in applied mathematics or statistics. 5+ years of industry experience. Advanced coursework in machine learning and programming. Experience with data querying languages, and statistical or mathematical software. Proficient in writing algorithms and knowing when to apply them. Excellent understanding of statistics, multivariable calculus, and linear algebra,

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