"Automated Reimbursement System
1. Clarifying Questions
Scope: Will the system be applicable to all employees or only specific grades/levels?
Ownership: Are we building this product internally for our own use, or is it intended for external/outsourced usage?
MVP Requirements: Besides automation, what additional features or problem statements should the Minimum Viable Product (MVP) address?
2. User Segmentation
Commute Expenses: e.g., m"
Kiran R. - "Automated Reimbursement System
1. Clarifying Questions
Scope: Will the system be applicable to all employees or only specific grades/levels?
Ownership: Are we building this product internally for our own use, or is it intended for external/outsourced usage?
MVP Requirements: Besides automation, what additional features or problem statements should the Minimum Viable Product (MVP) address?
2. User Segmentation
Commute Expenses: e.g., m"See full answer
"The product owner works closely with the tech team day to day, keeping track of the sprint stories and backlog and helping to unblock day to day blockers and dependencies. The product manager oversees the roadmap of features from the discovery to solutioning stage."
Anjali A. - "The product owner works closely with the tech team day to day, keeping track of the sprint stories and backlog and helping to unblock day to day blockers and dependencies. The product manager oversees the roadmap of features from the discovery to solutioning stage."See full answer
"A Random Forest works by building an ensemble of decision trees, each trained on a slightly different version of the data. The key mechanism is bagging: for each tree, we sample the training data with replacement (bootstrapping), so every tree sees a different subset of examples. On top of that, at each split the algorithm randomly selects a subset of features, so trees explore different predictors.
These two sources of randomness decorrelate the trees. When we aggregate them — by averag"
Yuexiang Y. - "A Random Forest works by building an ensemble of decision trees, each trained on a slightly different version of the data. The key mechanism is bagging: for each tree, we sample the training data with replacement (bootstrapping), so every tree sees a different subset of examples. On top of that, at each split the algorithm randomly selects a subset of features, so trees explore different predictors.
These two sources of randomness decorrelate the trees. When we aggregate them — by averag"See full answer
Data Scientist
Technical
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