Data Scientist – New York


The ideal candidate should possess hands-on engineering experience using machine learning and statistical tools to leverage data management and customer experience. This role will also be involved with our client’s Innovation team which aims at building up next generation digital platform to bring completely new experiences to their customers.


  • Excellent communication skills and ability to communicate with our clients in the US.
  • Bachelor’s degree in a computational science with over 2 years of postgraduate work experience in Machine Learning/Statistics/Data Science. Master’s degree is preferred.
  • Experience with traditional as well as modern machine learning/statistical techniques, including Regression, Classification, Regularization, Ensemble Methods, Deep Neural Network, Causal Inference and Hypothesis Testing.
  • 3+ years of experience with programming languages, such as Python, R, Java, C/C++; familiarity with Linux/Unix/Shell environments, Python preferred.
  • 3+ years of experience in working with large-scale database, experience with AWS products is preferred.
  • 2+ years hands-on skills in sourcing, cleaning, manipulating and analyzing large volumes of data.
  • 2+ years of experience in building and maintaining large scale data/machine learning pipelines in online advertising, recommender system, ecommerce or relevant areas.
  • Experience leading machine learning/data science projects.
  • Experience working with product, sales and other key stakeholders to drive business.


  • Gathering and analyzing data, performing POC analysis from a statistical perspective, identifying key factors, building prototypes and reporting findings to product owners at clients in the US.
  • Formulating machine learning/statistical/optimization approaches according to business metrics, designing features from rich data sources, training, evaluating and deploying models.
  • Implementing statistical testing to measure the success of projects and business impact.
  • Researching forefront techniques, proposing and initiating innovative data science efforts to drive management efficiency and member experience.
  • Designing and building up scalable data science pipelines, creating high-performance APIs within engineering according to business requirements, maintaining data science pipelines.
  • Constructing APIs for data gathering, exploring and performing QA for third party data.
  • Learning and sharing knowledge about data science, proposing the capability of data science to business team.