about project
Feature store + uplift models powering success playbooks in HubSpot.
- python
- dbt
- snowflake
- mlflow
Customer success chased NPS anecdotes while churn spiked silently among SMB borrowers.
We engineered Snowflake features (usage, repayment cadence, support tone) flowing into uplift models.
Playbooks deploy with experiment tracking and guardrails for fair lending reviewers.
Leaders inspect cohort lift weekly without waiting for BI tickets.
Features
- Feature store with lineage tags
- Uplift models with calibration monitors
- HubSpot playbook triggers
- Fairness constraint reporting
- MLflow versioning with approvals
Case
study
Approach
How we framed the challenge, shipped the solution, and measured outcomes.
Challenge
Stakeholders lacked a single dependable source of KPI truth.
Our approach
Shipped dashboards with guardrails and explicit ownership.
Results
CS play adoption rose with explainable rationales per account.
Key metrics
rate: +14 pts
triggered expansions: +9%
2 weeks automated
Work in stages
Requirement Gathering & Discovery
1
Project
However, all complete IoT systems are the same in that they represent the integration of four distinct components: sensors/devices, connectivity, data processing, and a user interface.
IOT Projects software
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