Facilitates the implementation of processes for machine learning (ML) model productionization to managers. Implements organizational standards around machine learning model readiness for deployment. Promotes organizational strategy around the automation of machine learning workflows. Promotes organizational strategy around trained model/system alignment with design criteria. Implements improvements to organizational processes for the identification and evaluation of potential data quality, security, and/or privacy issues and their impacts on modeling. Facilitates organizational troubleshooting and debugging support processes to address issues in machine learning infrastructure and workflow and create robust solutions. Alleviates the impact of obstacles to cross-functional collaboration efforts with multiple stakeholders to make, adopt and communicate technical decisions and shape the development and delivery of software. Implements organizational processes for the development, refinement, and maintenance of tools, platforms, environments,
and services for internal use. Implements improvements to organizational processes for the development of effective, bug-free code from scratch. Executes organizational strategy to maintain team awareness of current developments in the machine learning field and integration of this knowledge into model development.
Responsibilities
Key Responsibilities
Machine Learning and Data Modeling – Model Productionization:
– Facilitates the implementation of machine learning (ML) model productionization processes and process improvements.
– Uses technical knowledge and business familiarity to empower the transformation of machine learning prototypes into production-ready models.
– Implements strategy to build technical expertise and readiness across team related to model productionization.
– Alleviates the impact of obstacles on collaboration with multiple stakeholders, such as Development Leads, Prod