Home / About / Careers / Data Scientist

Data Scientist

Data scientist profile

We are looking for a Data Science and Machine Learning Engineer to join our R&D team.

You take the full responsibility for the design and implementation of data analytics and machine learning cycles:

  • Gather and analyse business requirements
  • Identify data sources
  • Build / use data pipelines or other mechanisms for data aggregation and consolidation
  • Design, test, measure and improve models and algorithms following short iterative cycles

The position is located in Toronto or can be assigned to a remote worker according to our WFA (work from anywhere) policy.  


About yourself

Skills and experience

  • MSc or PhD in Computer Science, Mathematics, Statistics.
  • Strong analytical background.
  • Proficiency in one or more machine learning platforms / languages such as R, Python, H2O, TensorFlow ...
  • 3+ years of professional experience in data analysis and modeling.
  • Good command of English (writing and speaking).
  • Very hands-on with the capacity to manage the day-to-day development activities.
  • Proven track record in driving small to medium sized projects to completion.
  • Demonstrated ability to achieve stretch goals in a highly innovative and fast paced environment.
  • Skilled at focusing and prioritizing work effectively to meet deadlines and achieve desired results.
  • Excellent problem solving and troubleshooting skills.
  • Ability to work well with people and be both highly self motivated and motivating.
  • Experience in communicating with business teams to collect requirements.


We’re looking for people who can adapt very quickly and with an open mind. Choosing and using the best tools for the job is a key success factor. It is therefore required that you get a good understanding of standard algorithms used in machine learning, including:

  • Classification methods (e.g., Neural Net, Logistic Regression, Decision Trees, KNN, SVM, RandomForest)
  • Regression methods (e.g., Linear, Nonlinear, Boosted Regression Trees )
  • Clustering methods (e.g., K-means, Fuzzy C-means, Hierarchical Clustering, Mixture Modelling)
  • Time-series Modelling/Forecasting (e.g., AR, ARMA, GARCH, Exponential Smoothing)
  • Statistical Analysis (e.g., Hypothesis Testing, Experiment Design, Hierarchical Modeling, Bayesian Inference)
  • Neural Networks


  • Experience with Spark
  • Experience with AWS and GCP cloud services