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Data Scientist (Artificial Intelligence)
City: Montreal or Toronto Province: Quebec Country: Canada
Posted: 5/31/2018 Expires: 8/31/2018 Start: ASAP
Type: Permanent Travel: NA Dress Code: Casual
 
Job Description:

The successful candidate will join a team who is responsible for maintaining the competitiveness of our client's predictive models. Building on our existing industry-leading GLM framework and unparalleled customer database, the team objective is to add-value through the integration of both new data sources (including Big Data) and the application the latest machine learning techniques. As well as contributing to the R&D agenda, the role will involve working closely with a variety of internal and external stakeholders, ensuring the alignment of priorities and maximising the usage of our insights.

Skills & Experience required

- Exceptional numerical ability and creative thinker/problem solver.
- Experience using some of the major data science tools e.g. Python (preferable), R, SAS, etc.
- Organized, autonomous, and a drive / commitment
- Excellent interpersonal skills: the candidate would be expected to work across and between existing teams.
- Strong communication and presentation skills, including the ability to translate analyses to non-technical audiences.

Desirable Skills

- Proficiency in statistical modelling and machine learning techniques e.g. k-means, decision trees, neural networks, ensemble learning, etc.
- Good working knowledge of SQL and understanding of data modelling and relational databases.
- Experience in handling big-data an advantage e.g. AWS/Spark/Hadoop.
- Experience of machine learning libraries e.g. scikit-learn, Tensor Flow.
- Experience in feature engineering and designing robust model tuning approaches.
- Knowledge of/experience with cloud-based applications and development in e.g. Amazon, Azure
- Experience with one or more programming languages C/C++, Java, FORTRAN, VB.net/C#, Python, etc.
- Familiarity with DevOps practices including source code management/version control e.g. git, SVN and automated testing
- Experience of natural language processing and associated techniques e.g. word-embedding, pos-tagging, etc. as well as associated software libraries e.g. StanfordCore NLP, NLTK, Spacy, etc.
- Knowledge of advanced AI techniques i.e. deep learning, reinforcement learning, etc.



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