Intel Data Scientist in Santa Clara, California
Uses predictive modeling, statistics, Machine Learning, Data Mining, and other data analysis techniques to collect, explore, and extract insights from structure and unstructured data . Develop software, algorithms and applications to apply mathematics to data, perform large scale experimentation and build data driven apps to translate data into intelligence, solve a variety of business problems and enable business strategy. Assists business with casual inferences & observations with finding patterns , relationships in data. Must possess strong understanding of internal business segment stakeholders and possess strong written and communication skills. Typically requires expertise in relational database structures, research methods, machine learning, Cloud based technologies, Big Data technologies i.e. Hadoop , HBase, Lucene/Solr, analytics packages i.e. R, Mahout, Matlab, Octave, Weka, scripting languages i.e. Python, Perl, programing languages i.e. Java, C/C++, SQL. Typically possesses advanced degree in Computer Science, Mathematics, Machine Learning, Operation Research, and Statistics or equivalent expertise.
You must possess the below minimum qualifications to be initially considered for this position. Experience listed below would be obtained through a combination of your school work/classes/research and/or relevant previous job and/or internship experiences. Minimum Qualifications:Candidate must possess a Masters or PhD Degree in Computer Science, Statistics, or any other related field.- 2 years of experience in the following fields:- Machine learning and deep learning methods for predictions.- Demonstrating the understanding of statistical inference and model comparisons, and in feature extraction.- Signal processing maximizing signal-to-noise and dimensionality reduction.- Open source machine learning and statistical software libraries e.g. Keras or other deep learning libraries, XGBoost, Caret, Scikit-Learn, Stan, PyMC, Spark MLLib, among others.- One or more programming languages such as Python, Scala, R, Matlab / Octave, Java, C++, Julia.- Data visualization and presentation skills.- Using Software engineering practices, e.g. version control software, unit testing, writing informative comments.
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