Principal Engineer - Data Science

• Adapting and enhancing machine learning techniques based on physical intuition about the domain
• Design sampling methodology, prepare data, including data cleaning, univariate analysis, missing value imputation, , identify appropriate analytic and statistical methodology, develop predictive models and document process and results
• Lead projects both as a principal investigator and project manager, responsible for meeting project requirements on schedule and on budget
• Coordinate and lead efforts to innovate by deriving insights from heterogeneous sets of data generated by our suite of Aerospace products
• Support and mentor data scientists
• Maintain and work with our data pipeline that transfers and processes several terabytes of data using Spark, Scala, Python, Apache Kafka, Pig/Hive Impala
• Work directly with application teams/partners (internal clients such as Xbox, Skype, Office) to understand their offerings/domain and help them become successful with data so they can run controlled experiments (a/b testing)
• Understand the data generated by experiments, and producing actionable, trustworthy conclusions from them
• Apply data analysis, data mining and data processing to present data clearly and develop experiments (ab testing)
• Work with development team to build tools for data logging and repeatable data tasks tol accelerate and automate data scientist duties

Requirements:

• Bachelor's or Master's degree in Computer Science, Math, Physics, Engineering, Statistics or other technical field. PhD preferred
• 8 to 10 years of experience in data mining, data modeling, and reporting
• 5+ years of experience working with large data sets or do large scale quantitative analysis
• Expert SQL scripting required
• Development experience in one of the following: Scala, Java, Python, Perl, PHP, C++ or C#
• Experience working with Hadoop, Pig/Hive, Spark, MapReduce
• Ability to drive projects
• Basic understanding of statistics hypothesis testing, p-values, confidence intervals, regression, classification, and optimization are core lingo
• Analysis - Should be able to perform Exploratory Data Analysis and get actionable insights from the data, with impressive visualization.
• Modeling - Should be familiar with ML concepts and algorithms; understanding of the internals and pros/cons of models is required.
• Strong algorithmic problem-solving skills
• Experience manipulating large data sets through statistical software (ex. R, SAS) or other methods
• Superior verbal, visual and written communication skills to educate and work with cross functional teams on controlled experiments
• Experimentation design or A/B testing experience is preferred.
• Experience in leading a team required.