Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex, high volume data from a variety of sources;
Analyzes large quantities of data and presents insights and predictions (e.g., on client behaviors and preferences, new products and services) to support management planning, execution and monitoring of business decisions; Builds and maintains the production execution of Data Science into enterprise systems and architectureYou will be accountable to:
Your experience includes:
- Monitor and deploy within the production data science environment using best practices, pipeline controls, and clear documentation around intent and usage of model or algorithm.
- Research, design, and construct predictive models to enhance understanding of Moneris core business and adjacent opportunities. Design and implement solutions that can measurably improve business performance in the business context.
- Partner with data engineering and data analytics teams to ensure that data structures and data pipeline enhances data science applications and shortens development cycle.
- Use robust statistical techniques to increase accuracy for existing projects and create solutions for adoption in business context knowingly increase performance.
- Lead in tuning and refining the deployment of models in a variety of environments ensuring that the business context matches the performance of the model and that the cost of running aligns with the need.
- Collaborate with different functional teams to promote structured pipeline deployment of data science to ensure consistency, security, resiliency of the system.
- Collaborate with business teams in the centre of excellence on data science sharing best practice, new approaches, and successes
- Actively build business and technical understanding to enhance solution development and create opportunities to enhance business processes and overall performance.
- Masters degree in in a quantitative field required, or equivalent work experience. PhD considered an asset
- Experience in Databricks
- Experience in Spark, Pyspark, SQL, Python
- Experience in Azure
- Experience in Power BI, Tableau
- Experience in Machine Learning methodologies and has experience building production grade solutions
- Minimum 5 years experience in analytics, data science, computer engineering, database management
- Proficient in multiple programming languages and can code with no oversight.
- Experience in production data science pipeline management and deployment of models in a production environment.
- Highly proficient in leading large scale projects or significant project steps and communicating progress/approach with technical/non-technical peers/clients and leaders.