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Modeled loss ratios with Weka decision tree (Reptree and M5P algorithms) machine learning systems.Assist actuaries with technical price development using data driven insights resulting from analytics.Develop claims propensity models to proactively identify accounts with high probability of a significant loss.For commercial accounts, develop ground up and residual pricing models to determine optimal premium based on account risks.Tools: Hadoop HDFS based mufti node cluster, Hive, Oozie, Apache Spark/Scala, Maven and SBT build tools
In the process of developing/deploying self-calibrating models with Apache Spark - Scala and Java on Mahout/MLlib/Weka based Machine Learning
Teradata studio not detecting java 8 mac full#
ETL and distributed data storage: ETL and Data Storage: Designed and implemented a large scale (several Terabytes) data warehouse on Hortonworks HDFS multi-node cluster with full suite of Hadoop ecosystem - Yarn, Map-Reduce, Tez, Oozie, Sqoop and Hive.Leverage technology, employ pseudo automated quantitative methods with Machine Learning to optimize digital media spend and generate revenue lift through targeted online campaigns.
Teradata studio not detecting java 8 mac plus#
Tools: Base SAS plus SAS Enterprise Miner on Solaris/SunOS
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Incorporated sensitivity analysis into the forecasting process to determine key drivers of demand and the Net Present Value (NPV) of the expected revenue over the entire demand cycle.
Teradata studio not detecting java 8 mac mac os#
Operating Systems: Debian/RHEL, Mac OS X, Windows Server Hadoop ecosystem Hive/SQOOP/OOZIE, Apache Spark with Java/MLlib for Machine Learning application developmentĭata Warehousing: DB2, Oracle, SQL Server, Teradata and Informatica
Statistical Data Mining software: SAS, KXen, SPSS, SAS Enterprise Miner, SAS EG Custom Algorithm development and maintenance Recommender Systems and Collaborative Filtering algorithmsīig Data Hortonworks/Cloudera based multi: node cluster setup on RHEL/Debian Linux. Other Marketing Mix models such as Market Basket Analysis (Association Rules), Demand / Revenue Forecasting Statistical Data Mining techniques: Data Exploration, Significance Testing, Regression (Multinomial/LOGIT), Clustering (K - Means), Segmentation (C4.5, CHAID).
I have over 10 years of statistical model development experience, as well as experience in Customer Profiling based on needs, motivations and behavioral preferences. I specialize in Machine Learning algorithm development and ETL tool development where he uses languages such as Java, J2EE, Python, R, SAS, and C++. I’m a highly skilled Data Scientist with over 15 years of experience in Database Marketing, Data Warehousing and Statistical modeling development in a variety of industries including retail, CPG, financial services and healthcare.