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 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 center 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 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
• Statistical background required, knowledge of root cause analysis
• 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.
Nice to Have
• Proficiency in payment systems is an asset.