• Work in a cross-functional team to define problems, quantify metrics, explore data, build analytical models, conduct experimentation, and make recommendations.
  • Design, build, deploy and maintain machine learning service.
  • Communicate findings and recommendations to all stakeholders as well as drive decision making.
  • Rapidly prototype methods for internal experiments & early R&D.
  • Explore state-of-the-art data science and machine learning research.


  • BS (or higher, e.g., MS, or PhD) in quantitative field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research or other related field). 
  • Good communication with strong analytical and problem-solving skills. 
  • Experience with Machine Learning, Statistics, or other data analysis tools and techniques.
  • Experience in extraction, cleaning, analysis, and presentation for medium to large datasets. 
  • Experience with at least one programming language (e.g., Python, R, Scala).
  • Experience with tools for data visualization (Matplotlib, Tableau, or Qlik), scientific computing (e.g., NumPy, SciPy, Pandas, Scikit-learn, dplyr, or ggplot2) and machine learning (e.g., PyTorch, Caffe2, TensorFlow, Keras or Theano). 
  • Experience with statistics methods such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis.
  • Experience working with Cloud platform (e.g., AWS, Azure, GCP).
  • Experience with one or more advanced machine learning topics (e.g., OCR, Image Recognition, NLP, Recommendation System) considered a plus.

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