Details:

Job Description and Responsibilities

Requirements :

- Should have worked on data heavy projects on risk scoring, propensity / classification modeling, segmentation, promotion analytics, campaign optimization, recommender engines, fraud analytics, etc.

- Strong hands on programming experience in SAS / Tableau / R / Knime and knows the best coding practices.

- Great analytical skills, with expertise in Machine Learning methods such as supervised and unsupervised learning, SVM, Neural Networks, Nave Bayes Classifier, Decision Trees, PCA etc. is MUST.

- Able to create / develop a compelling story to answer business questions using analytics outcomes.

- Must possess good client facing experience with the ability to facilitate requirements sessions and lead teams and present analysis in business terms

- Exposure to Big Data technologies Hadoop, Hive, PIG, MapReduce, SparkML, Python, Splunk will be given priority.

Min. Qualification:

ug

Skills Required:

classification, decision trees, Fraud Analytics, hadoop, machine learning, mapreduce, naive bayes, neural network, python, r, sas, segmentation, splunk, supervised learning, tableau, unsupervised learning