Senior Applied Machine Learning Engineer - Remote

Responsibilities

• Improve existing churn, retention, and account segmentation models using customer data.
• Evaluate predictions against actual renewal, churn, expansion, and adoption outcomes.
• Build time-based backtests that prevent models from accessing future information.
• Investigate incorrect predictions account by account and identify gaps in labels, features, or source data.
• Ship improvements into the existing Go product, rather than stopping at analysis notebooks.
• Explain model performance and prediction errors to customers in clear business terms.
• Keep internal queries and training datasets confidential when discussing results.
• In the first 30 days, run recall analysis for every existing customer using fixed test sets and ship improvements identified through that analysis.
• Within 3–6 months, automate evaluation and flag drops in recall, label quality, or product-usage signals before customers report them.
• Build and validate new models for expansion, cross-sell, forecasting, and marketing use cases.

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