• Own diagnostics, insights, and tuning for AI Decisioning campaigns • Explain why AI Decisioning is driving lift using counterfactuals, incrementality breakdowns, and cohort analysis. • Debug performance issues, iterate on reward functions, and ensure the agent’s recommendations align with customer goals. • Investigate experiment setups (send volumes, reachability, channel constraints) and surface actionable recommendations. • Work deeply with data in notebooks and customer warehouses • Pull down historical data to run exploratory analyses using Polars / Pandas in Jupyter notebooks • Modify and improve customer feature matrices to unlock deep personalization. • Conduct deeper warehouse-level SQL analyses when insights aren’t available in the UI. • Build lightweight tooling that enables scale • Create templates, notebooks, scripts, and repeatable workflows that improve how we analyze performance across customers. • Identify systemic gaps and influence the direction of ML reporting and introspection. • Communicate ML concepts clearly to non-technical stakeholders • Present model insights and recommendations to marketers, analysts, and executives. • Explain how the decision engine handles cold start, message transfer learning, exploration vs. exploitation, and more. • Partner closely with Solutions Consultants to identify and drive new opportunities for uplift.