FINTECH – Machine Learning Engineer/Developer

Posted 1 month ago

The Role:

Machine Learning Engineer/Developer, with a background in Financial Markets, to be directly involved in advancing the company’s data analysis and machine learning capabilities. The company is expanding aggressively. We are looking for individuals with trading domain knowledge, who can help develop and support a robust financial trading infrastructure. You’ll be working on cutting- edge Quantitative Data Analysis and Machine learning challenges. Location: Toronto or remotely – candidate’s choice. Full-time, permanent position with competitive salary, and an excellent working environment.


  • Ph.D. Engineering, Ph.D. Applied Mathematics or similar.
  • Python/C++/R/Java.
  • C#/.NET.
  • TensorFlow/Caffe/Torch.
  • Probability and Statistics.
  • Applied Mathematics: convex optimization, quadratic programming, partial differential equations, etc.
  • Strong foundation in data modelling, statistics and an academic background in applied mathematics and data science.
  • Expertise in curating and preparing data for use within supervised and unsupervised machine learning projects.
  • Comfortable manipulating large data sets, comprising both numerical and categorical data.
  • Knowledge of ML algorithms, decision trees, SVMs, neural networks and the ability to apply the correct learning environment to given problems.
  • Knowledge of ML/AI tools such as TensorFlow, Caffe & Torch.
  • Ability to pull data from HDFS data lakes or databases.
  • Ability to fuse and correlate different data feeds.
  • Up to date subject knowledge, and ability to remain abreast of current developments in the field (tools, conferences, blogs).
  • Understand capital markets concepts and the associated quantitative financial methods.
  • The right candidate must be able to demonstrate strong communication skills, creative solutions, and results-driven behaviour in time-sensitive situations.
  • A proactive, self-motivated, and team-oriented analytical thinker.
  • Strong conceptual, innovative, and problem-solving abilities.
  • Ability to look at data, visualize it, question it and draw conclusions from it.
  • Have a hands-on approach and the ability to apply many different methods and techniques in a flexible manner, to generate actionable ideas.




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