• Providing data analytics support for the ML and AI models that underpin our core products • Partnering with machine learning researchers and data science stakeholders to design experiments to answer business questions, including defining key metrics and applying appropriate statistical techniques to analyze results • Supporting a culture of experimentation to promote faster cycles of iteration, learning, and product improvement • Build dashboards to monitor the performance, inference, and application of core models. • Conduct investigations into trends and anomalies in model performance and data drift • Developing attribution methodology to measure the contribution of deployed models to company KPIs • Simulate proposed changes to core systems (e.g. recommendation, filtering, etc.) to support the strategic decisions around model development and deployment.