- Partner with Data Scientists and ML Engineers on modeling trade-offs, ground-truth definitions, training data quality, precision and recall, and feature strategy.
- Navigate ML infrastructure decisions, including model serving, feature pipelines, batch versus real-time inference, latency, and production dependencies.
- Turn ambiguous business problems into model objectives, technical requirements, and measurable success criteria.
- Own and prioritize the ML roadmap across verticals, balancing business impact, technical feasibility, and engineering capacity.
- Work across Data Science, Data Engineering, Product Engineering, and business teams to integrate model predictions into consumer experiences, decisioning systems, and workflows.
- Ensure launches are production-ready, dependencies have clear ownership, and performance is validated against technical and business requirements.
- Work with Data Science and Analytics on experiments, A/B tests, and causal measurement of ML's incremental impact.
- Define and monitor predictive performance, reliability, adoption, and business outcome metrics.
- Explain model performance, trade-offs, limitations, and revenue impact to senior stakeholders.
- Set the long-term vision for ML products and mentor product managers and partners.
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