Overview
On Site
Depends on Experience
Contract - W2
Contract - 12 Month(s)
Skills
Industrial Engineering
Data Deduplication
Data Mining
Decision Trees
EDA
Extraction
HDFS
Java
LSA
Logistic Regression
Machine Learning (ML)
Management
MapReduce
Apache Spark
Apache Sqoop
Big Data
Business Objects
Advanced Analytics
Apache HBase
Apache Hadoop
Apache Hive
Apache Kafka
Database
Mathematics
Microsoft Power BI
Named-Entity Recognition (NER)
Cluster Analysis
Computational Linguistics
Computer Science
D3.js
Pattern Recognition
Programming Languages
SAS
SPSS
SQL
Scala
Semantics
R
Software Packaging
Statistics
Tableau
Training
Visualization
Weka
Analytics
Data Analysis
Natural Language Processing
Operations Research
Python
k-means clustering
Job Details
Bachelor s Degree (required), Master s or Ph.D. degree (preferred) in operations research, industrial engineering, mathematics, statistics, computer science/engineering, or other related technical fields with equivalent practical experience.
Required Qualifications
Proficiency with statistical software packages: R
Experience with programming languages: R, SQL
Experience constructing and executing queries to extract data for exploratory data analysis and model development
Experience performing training set construction, analysis, and data mining
Experience with unsupervised machine learning techniques and methods
Significant experience in developing machine learning models and applying advanced analytics solutions to solve complex business problems
Proficiency with SQL programming
Experience with unsupervised and supervised machine learning techniques and methods
Experience working with large-scale (e.g., terabyte and petabyte) unstructured and structured data sets and databases
Experience performing data mining, analysis, and training set construction
Desired Qualifications
Experience with programming languages including: Python, Scala, Java
Experience constructing and executing queries to extract data in support of EDA and model development
Proficiency with statistical software packages including: SAS, SPSS Modeler, R, WEKA, or equivalent
Proficiency with Unsupervised Machine Learning methods including Cluster Analysis (e.g., K-means, K-nearest Neighbor, Hierarchical, Deep Belief Networks, Principal Component Analysis), Segmentation, etc.
Experience with Natural Language Processing (NLP), computational linguistics, Entity extraction, named entity recognition (NER), name matching, disambiguation, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA).
Proficiency with Supervised Machine Learning methods including Decision Trees, Support Vector Machines, Logistic Regression, Random/Rotation Forests, Categorization/Classification, Neural Nets, Bayesian Networks, etc.
Experience with pattern recognition and extraction, automated classification, and categorization
Experience with entity resolution (e.g., record linking, named-entity matching, deduplication/ disambiguation)
Experience with visualization tools and techniques (e.g., Periscope, Business Objects, D3, ggplot, Tableau, SAS Visual Analytics, PowerBI)
Experience with big data technologies (e.g., Hadoop, HIVE, HDFS, HBase, MapReduce, Spark, Kafka, Sqoop)
Master s Degree in mathematics, statistics, computer science/engineering, or other related technical fields with equivalent practical experience
Clearance:
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