Business Context

Nimbus Analytics has a 42-person product analytics team supporting multiple product lines. Over the last 2 quarters, voluntary attrition on the team increased from 8% annualized to 15% annualized, and the VP wants an early-warning framework for identifying retention risk before people resign.

Metric Scenario

The team currently has 42 employees: 8 joined in the last 6 months, 21 have been on the team 6-24 months, and 13 have 2+ years tenure. In the last employee pulse survey, overall engagement fell from 7.8 to 7.1 out of 10. Manager 1:1 completion dropped from 92% to 76%, internal mobility applications rose from 3 to 9 in a quarter, and average weekly after-hours work increased from 3.2 to 5.6 hours. Regrettable attrition in the last 12 months was 5 employees, with 4 of those employees showing lower survey scores, fewer 1:1s, and higher after-hours work in the 60 days before leaving.

Leadership asks you to define how you would measure retention risk for this team, what leading indicators matter most, and how you would segment the metric to find where risk is concentrated.

Requirements

1. Define a primary retention risk metric for the team and explain why it should be tracked alongside actual attrition.
2. Propose 3-5 leading indicators and explain how each should be interpreted.
3. Show how you would decompose retention risk across team segments.
4. Identify thresholds or benchmark ranges that would trigger intervention.
5. Recommend actions for high-risk segments and guardrails to avoid overreacting to noise.

Data Available

• employee_roster: employee_id, manager_id, role_level, tenure_months, location, start_date
• pulse_surveys: employee_id, survey_date, engagement_score, manager_support_score, career_growth_score
• manager_1on1_logs: employee_id, scheduled_1on1s, completed_1on1s, week_start
• work_patterns: employee_id, week_start, after_hours_hours, meeting_hours, PTO_days
• hr_events: employee_id, transfer_application_date, promotion_date, exit_date, exit_type