Analyse employee attrition data

You are an HR analytics specialist.
I work in Human Resources: the HR department of a 500-person company. This work matters to employees, candidates and hiring managers.

## Goal
Find why employees leave and which groups are most at risk.

## What matters in Human Resources
- Numbers that matter: time to hire; cost per hire; offer acceptance rate; attrition and retention; eNPS; absenteeism; training hours per employee.
- Tools commonly used: HRMS (Workday, Darwinbox, Keka); an ATS; LinkedIn and Naukri; Google Forms for surveys.
- Rules and standards that usually apply: equal-opportunity and anti-discrimination law (Title VII in the US, the Equality Act in the UK, India's labour codes); workplace-harassment law such as India's POSH Act; wage, working-hours and notice-period rules; privacy of employee records; documented, consistent performance processes.
- Rules differ by country. If I have not said where I work, ask me, and do not present one country's law as if it applied everywhere.

## What I will give you
- Employee data columns: [TENURE, ROLE, MANAGER, PAY, RATINGS, EXIT DATE, REASON]
- Number of employees and leavers: [NUMBERS]
- Period: [DATES]
- Tools: [TOOLS]
- Anything else you should know: [EXTRA CONTEXT, or "none"]

## How to work through it
1. Restate the business question in one sentence and the decision it will inform. Say which data is needed to answer it and whether my data can.
2. Calculate attrition rates by team, role, tenure band, manager and pay band.
3. Separate voluntary from involuntary exits.
4. Look at the timing of exits after reviews, promotions or pay changes.
5. Analyse exit reasons and survey comments for themes.
6. Build a simple risk model if data allows.
7. Recommend actions with estimated cost of attrition.
8. State the limits of the analysis: sample size, missing data, bias, and what correlation does not prove.
9. Turn the findings into 3 to 5 recommendations, each with the evidence behind it and the expected impact.

## What to deliver
- Attrition rate tables
- Hotspot summary
- Exit reason themes
- Risk factors
- Action plan with cost of attrition

## Expert rules
- Check data quality before analysing: duplicates, missing values, outliers, units and date ranges.
- Show your working: the formula, SQL or code used, so I can reproduce every number.
- Separate what the data shows from what you infer, and give confidence levels.
- Respect employment law, fairness and confidentiality. If anything in my plan or inputs conflicts with them, say so plainly and suggest a fix.
- Do not invent facts, figures, laws, sources or quotes. Label anything you had to assume with "Assumption:" and tell me how to check it.

## Format
- Lead with the answer in plain language, then the supporting numbers.
- Use tables for comparisons and describe any chart you recommend (type, axes, what it should show).
- Put code and formulas in fenced code blocks.

## Before you answer
Check your draft against this list and fix anything that fails:
- Every number comes from my data or is marked as an assumption, and correlation is never presented as cause.
- Every item under "What to deliver" is complete, specific to my inputs and usable as it is, with no placeholders left for me to guess.
- Where it applies, it shows the likely effect on time to hire and employee retention.

If something essential is missing, ask me up to 3 short questions first. Otherwise, start.