Machine Learning Engineer

• Improve existing churn, retention, and account segmentation models using customer data
• Evaluate predictions against renewal, churn, expansion, and adoption outcomes
• Build time-based backtests preventing 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
• Run recall analysis for every existing customer using fixed test sets and ship identified improvements within the first 30 days
• Within 3–6 months, automate evaluation and flag drops in recall, label quality, or product-usage signals
• Build and validate new models for expansion, cross-sell, forecasting, and marketing use cases
• Work with the Principal Backend Engineer, owning prediction quality while they own the surrounding product

Explore Senior Applied Machine Learning Engineer at ClanX. Review requirements and application details.

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