Job description

Want to be part of the team that changes how A.I and humans interact?

We are looking for Data Scientists to help bring A.I-human interaction to the next level!

Nudgyt is an A.I company that is the first to combine Behavioural Science and Artificial Intelligence to create Personality specific nudges to help people understand data and drive action towards desired goals.

and you'll get:

- SGD 5,000 to 10,000 monthly salary - more if you're good! Prove yourself!

- We treat our team like adults. Flexible office hours, work from anywhere arrangements, personal time off - so long as you show us your best work!

- Learn from and work with the top data scientists in Asia - Informal schooling, self-taught are welcome, you're portfolio is most important

  • Remote work allowance

Duties and Responsibilities

  • Researching or building models that best relate the the use case for the particular project.
  • Weigh pros and cons of various models and present recommendations for the best implementation
  • Improving model accuracy based on user behaviour on the system.
  • Work with development team to implement A.I model microservices into the product.

Skills and Competencies

  • At least 1+ years of experience in Data Science or Mathematical Statistics.
  • Have deployed Machine Learning Models into a commercial product.
  • Tensorflow
  • Experience in designing, deploying or maintaining end-to-end ML systems in production using Docker/Kubernetes
  • Keras
  • Python
  • Familiarity with cloud platforms like AWS or Azure

Nice to have

  • History or background knowledge of behavioural science concepts and authors like Dan Ariely, Richard Thaler or Angela Duckworth.
  • Understanding of Edge computing, Hidden Markov Models and Networked Learning models
  • Clients must have actively used deployed Machine Learning products.
  • Understanding of edge computing
  • Understanding of A.I chatbot models.

Job Factors

Willing to work on a results basis (this means pushing hard when deadlines loom but relaxing and learning when there is more time.)

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