[961] Data Scientist

Salary: $2000 - $3000 - Location: Remotely - Full-time


  • Primarily be a self-managing individual contributor. Research and develop new cutting-edge AI algorithms using techniques such as supervised/unsupervised ML, statistical/Bayesian inference etc. to power the AI & Analytics Engine with better, smarter, and more diverse recommendations for the end users;
  • Bring your novel ideas to a production-ready state by implementing them as modules, while adhering to industry standards in coding: quality, maintainability, and in conformance with the overall architecture are of paramount importance;
  • Participate in product-steering sessions in your technical capacity to:
    • Pitch new ideas to improve the product;
    • Respond to user feedback and business requirements by providing the necessary technical directions from a Data Scientist’s perspective, to put items on the product roadmap;
  • Mentor and provide training to graduate-level associate data scientists and interns, motivating them to reach the next level;
  • Participate in the delivery of our product to customers and other customer facing activities;
  • Participate in technical content writing. This may be in the form of product documentation, blog articles highlighting new features and use cases, as and when the need arises;
  • Understand and advocate our long-term vision while working with the management and product teams to define and adapt the same; and
  • Contribute to the evangelization of our product and our culture internally and externally.


Fluency in English both written and spoken

  • Experience with multiple ML/DL frameworks (scikit-learn, Dask, Spark Mllib/ML, XGBoost, LightGBM, TensorFlow, Torch, etc.)
  • Understand the scalability, complexity, memory usage and other performance issues of various algorithms;
  • Ability to write code that can be run in production in at least one of the following languages: Python, R, Scala, Julia, Java, C++, Go.

  • Have expert understanding and more than 3 years of proven R&D experience in at least one ML/AI professional field below:

    - DL framework for modern NLP/NLU

    - Extend supervised and unsupervised ML algorithms to big data scale; 

    - Hyperparameter search and model selection (Bayesian optimization)

    - PU / Active learning; 

    - Time series analysis (classification, prediction and survival analysis)

    - GPU acceleration of ML/DL; 

    - Explainable artificial intelligence; 

    - The application of graph models in artificial intelligence, such as Bayesian networks, Markov random fields, etc. 

    How to Apply

    Please send your profile to or ping me via skype hahoainhi

    Thank you!

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    Nhi Hà

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