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Pre Sales Consulting

1.00 to 5.00 Years   Bangalore   08 May, 2019
FICO
Job LocationBangalore
EducationNot Mentioned
SalaryNot Disclosed
IndustryIT - Software
Functional AreaPre-Sales
EmploymentTypeFull-time

Job Description

Have done data science in:Marketing analyticsDemand forecasting for manufacturing/retail.Anomaly detectionPrice elasticity modeling outside of financial services (retail, manufacturing)Sales effectivenessProject selection and planningSkills:

  • Method: Built model to predict Page view, model to predict conversion, model for price elasticity and hierar- chical clustering on attrbutes of the product to find similar products.
  • Algorithm: Used ROCK clustering and Hierarchical clustering to find similar products.
  • Used Gradient Boosting machine with 7 way interactions, Randomforest, Support Vector Regression (with logit link) for Page View, Conversion and Price Elasticity model. For feature engineering used PCA (to calculae spec-score of mobile), Box Cox transformation. Used chained imputation using CART to impute missing value.
  • Statistics Ensemble Models (Random Forest, Gradient Boosting Classifier),Machine Learning,
  • Collaborative filtering system for recommendation, Linear Regression, Logistics Regression, Clustering, Variable Clustering, Principal Component Analysis, Decision Tree (CHAID, CART)
  • Software Python (Pandas, Sklearn,NLTK, Numpy), SAS 9.3 Base, SAS EG(5.1), SAS Enterprise Miner (12.1), SPSS (15.0), Microsoft Office, Microsoft SQL Server Management Studio, SAS Text Miner, Tableau, Hands on R
  • ESM, ARIMA, SARIMA, ANN (Artificial Neural Network)
  • Big Data Technologies Hadoop, MapReduce, YARN, Zookeeper, Storm, Kafka, Knox,Argus, Slider, Tez, HUE, Hortonworks, Cloudera, PHD, HAWQ
  • Hadoop, Hive, Python, R, Pandas, Impala, Random Forest, Extreme Gradient Boosting, Spark
,

Keyskills :
sapanalyticsjavaintelligencephplogisticsrtificialfinancialdataservices

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