🫧 Predicting Behavioral Risk Trajectories

TL;DR

Built a modeling pipeline that predicts risk trajectories (how risk evolves over time) rather than a single static score. This is a better fit for interventions because timing matters.

Problem

Many risk models output one number (“high vs low risk”), but teams need to know:

• who is at risk,
• when risk is rising,
• which behaviors are driving the change.

Approach

• Data: longitudinal behavioral signals (multi-week windows) + optional survey/assessment anchors.
• Modeling: trajectory-aware risk modeling (sequence features + interpretable groups).
• Interpretability: summarize drivers per trajectory (what changed, not just how much).
• Evaluation: predictive metrics + calibration + subgroup/slice checks.

Results (what to add)

Replace this section with one chart and 2–3 bullet results:

• Baseline vs model (AUC/PR/MAE)
• Calibration (reliability curve)
• Example: trajectory segments that triggered an intervention

My role

• End-to-end pipeline: data cleaning → feature design → modeling → evaluation → write-up.
• Communicated trade-offs and deployment constraints for a product/ops audience.