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.