Your responsibilities include automating scoring using machine learning techniques, building recommendation systems, improving and extending the features used by our existing classifier, developing internal testing procedures, and building systems for automated fraud detection.
Key responsibilities
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 transparent manner
Creating automated anomaly detection systems and constant tracking of its performance
Technical requirements
Excellent understanding of machine learning techniques and algorithms, such as k-nn, naive bayes, svm, decision forests, etc.
Knowledge of common data science toolkits, such as r, weka, numpy, matlab, etcExcellence in at least one of these is highly desirable.
Knowledge of 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, statistical testing, regression, etc.
Good scripting and programming skills
Advanced ms excel skills
Data-oriented personality
Professional attributes
Excellent communication skills
Attention to detail
Analytical mind and problem solving aptitude
Strong organizational skills
Visual thinking
Education: master`s degree in statistics, data science, mathematics, or equivalent master`s degree program focusing on statistical methods.
Experience: 0 – 1 years
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