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Principal Data Scientist - Predictive and Prescriptive Analytics

Company Name: Verizon
Location: Basking Ridge, New Jersey
Date Posted: 14th Jan, 2018
Description:

Responsibilities

  • Your role will be multi-faceted. In addition to model development, you will take an active role in making assessments and recommendations on current and future technology, and helping to set the Platform architecture vision and roadmap.
  • You will work with domain experts to understand data sources and complete feature engineering. You will then perform data munging, and create training data sets, finally designing and executing experiments with the training data sets to determine the best models to apply, and performing cross validation to test the efficiency of the resulting algorithms.
  • When algorithms reach adequate efficiency thresholds, you will assist in implementing them into the Ann Real Time Decision Engine for run-time scoring, or ingestion into the Ann Real Time Customer Insights model store for batch scoring of customers.
  • The key to your success will be your ability to fully immerse yourself in the business problems you are solving, and acting independently to move projects to completion with minimal day to day oversight. You will be a hands-on data scientist responsible for the entire end to end process of model development, implementation, and maintenance of business-changing models and algorithms.
Qualification:

You'll need to have:

  • Bachelor's degree or four or more years of work experience.
  • Six or more years of relevant work experience.

Even better if you have:

  • Advanced degree in Statistics or Computer Science
  • Expertise and experience using R, Python, and Spark
  • Expertise and experience using Hadoop/HDFS
  • Expertise and experience using H20.ai
  • Expertise and experience using Spark ML
  • Familiarity with Tensor Flow
  • Familiarity with Big Data Cloud platforms, in particular AWS EMR
  • Familiarity with Pega Systems’ Decision Hub
  • Familiarity applying machine learning to solve other AI problems such as NLU/NLP, Voice Biometrics, Text Mining, and Intelligent Process Automation

Key Competencies

  • Strong analytical and research capabilities
  • Expertise in statistical techniques and quantitative methodologiesthat are used in Machine Learning applications
  • Demonstrated ability to evaluate machine learning tools, platforms, and architecture
  • Aptitude for synthesizing information and conveying complicated information clearly