Lead Data Science Engineer
- Research and implement DS applications in Python
- Create statistical and machine learning models and integrate them into DS applications
- Work with and help develop our MLOps pipeline
- Data engineering of data assets to assist in DS application development
- Writing production quality code to deploy and run models in a sportsbook
- Create automatic tests to ensure accuracy and reliability of DS applications
- Collaborate closely with product, developers, QAs and delivery leads to move projects from ideation to development and deployment
- Test that data flows work as expected and that DS applications are well integrated in larger business context
- Build advanced analytics tools for sportsbook product teams
- Coach and support more junior data scientists within the team
- Ideally, you have experience as the expert on a data science team where you were responsible for all aspects of data science technical projects, including development and deployment, and then monitoring of how those applications perform in production
- Experience leading and coaching other data scientists
- Strong understanding of object-oriented programming principles with Python
- Understanding of different database technologies, including relational and non-relational databases
- Experience implementing ML automation, MLOps principles and related tools
- AWS experience will be considered an asset
- Experience with typical DevOps flows, such as containerisation (e.g. Docker) and monitoring (e.g. ELK, Grafana)
- Experience with Kubernetes and Kafka is very desirable
- Knowledge of web frameworks (e.g. Flask, Django) and APIs is desirable
- Keen interest in sports
- Understanding of Sportsbook products will be considered an asset
- PhD, Masters or Bachelor's degree in STEM fields
- Excellent communication & organizations skills with the ability to articulate key business information quickly and effectively
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