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Research Data Scientist
Bellevue, WA
The Infrastructure Quantitative Engineering group is responsible for the strategic analysis to support and enable the continued growth critical to Facebook’s infrastructure organization. We are applied quantitative and computational experts using math, statistics and machine learning to measure & optimize cost, performance, reliability and efficiency of Facebook’s infrastructure & global telecom systems to deliver the best experience to our global audience. The ideal candidate will be passionate about Facebook, have strong analytical and modeling aptitude and has experience using data to drive cost effective decision making.
Research Data Scientist Responsibilities
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Build pragmatic, scalable, and statistically rigorous solutions to large-scale web, mobile and data infrastructure problems by leveraging or developing state-of-the-art statistical and machine learning methodologies on top of Facebook’s unparalleled data infrastructure
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Work cross-functionally to define problem statements, collect data, build analytical models and make recommendations
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Build and maintain data driven optimization models, experiments, forecasting algorithms, and machine learning models
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Leverage tools like Python, R, Hadoop & SQL to drive efficient analytics
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Communicate final recommendations and drive decision making
Minimum Qualifications
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Degree in quantitative field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research or other related field)
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2+ years of industry or graduate research experience solving analytical problems and building models using quantitative, statistical or machine learning approaches
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Experience with Machine Learning, Statistics, or other data analysis tools and techniques
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Experience performing data extraction, cleaning, analysis and presentation for medium to large datasets
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Experience with at least one programming language (i.e. Python, R, Java, or C++)
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Experience writing SQL queries
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Experience with scientific computing and analysis packages such as NumPy, SciPy, Pandas, Scikit-learn, dplyr, or ggplot2
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Experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis
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Experience with data visualization libraries such as Matplotlib, Pyplot, ggplot2
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Experience with machine learning libraries and packages such as PyTorch, Caffe2, TensorFlow, Keras or Theano
Preferred Qualifications
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Advanced degree (Master’s or PhD or Equivalent experience) in quantitative field
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Experience working with distributed computing tools (Hadoop, Hive, Spark, etc.)
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Proficiency in algorithmic complexity
