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Tech + Business
Organizational Retention Audit
ML-driven audit system mapping technical engagement signals to long-term turnover risk.
Product Technologist
The Challenge
“Can we predict organizational churn using non-traditional, low-bias engagement signals?”
Strategic Approach
Technical Implementation
- Decision: Prioritized Explainable AI (XAI) over raw predictive accuracy to build stakeholder trust.
- Constraint: Data must remain anonymized and compliant with organizational privacy policy.
- Trade-off: Used simpler linear models for transparency rather than black-box deep learning.
Business Impact
- ⭐Translates 'Employee Sentiment' into 'Financial Opportunity Cost'
- ⭐Allows for targeted retention spend instead of global across-the-board increases
- ⭐Demonstrates high empathy for the 'Human Systems' that drive business outcomes
Ambiguity Handling
“The primary challenge was validating engagement signal causality vs correlation in turnover prediction.”
Strategic Assumptions
- •Assumed engagement signals correlate with turnover intent
- •Assumed XAI increases stakeholder trust vs black-box models
Value Alignment
Mapping specific engineering achievements to strategic organizational outcomes.
Engineering MetricStrategic Impact
XAI Transparency
Executive Decision Buy-in
ML Attrition Lead
Proactive Retention Savings
Integrated Narrative
“My MBA focus on HR revealed that most churn is reactive. My CSE background allowed me to build a proactive audit tool that makes the 'Why' clear to executives.”
Technology Stack
React
FastAPI
Scikit-Learn
Recharts
Business Frameworks
Human Capital Valuation
Trust-First AI Strategy