Data Scientist - People Analytics (AutoPH Team)

About HighRadius!

HighRadius provides a single Agentic AI platform for the Office of the CFO. It integrates 180+ agents that orchestrate end-to-end processes across Order-to-Cash, Close & Reconciliation, Consolidation & Reporting, Accounts Payable, B2B Payments, and Treasury. HighRadius guarantees operational KPI improvements by mapping them to specific agents on the platform. With a 3-6 month go-live period, HighRadius drives value creation at 1300+ enterprises such as 3M, Unilever, Bristol-Myers Squibb Company, Red Bull, Lufthansa, and more. HighRadius has been consistently recognized as a market leader by Gartner, IDC, and Forrester.

Job summary

We are redefining how performance is measured and rewarded. As a Data Scientist within the AutoPH Strategy Team, you won’t just be analyzing data—you will be architecting the algo that drives our next-generation Performance Management System.

Contributing directly to the CEO’s strategic vision, you will engineer the correlation between behavioral inputs and business outputs, and build the algorithms that ensure our organization remains a meritocratic, high-growth environment.

1. Algorithm Engineering & PMS Innovation

• The AutoPH Engine: Own the statistical logic behind our "Autonomous Performance@ HighRadius" system. You will design, test, and iterate on algorithms that balance Output (results), Input (effort), and Culture KPIs.

• Model Evolution: You will critically evaluate our "AutoPH" logic and identify where our measurement systems lack precision. You will engineer the enhancements required to make our performance algorithms mathematically sound and bridge the gap between what we currently track and what we actually aim to measure.

• Predictive talent strategy: Move from reactive reporting to proactive intervention. You’ll implement ML frameworks (Time Series, Clustering, LLMs among others) to predict talent density, flight risks, and high-potential trajectories.

• From Concept to Code: Develop Python scripts to transition theoretical HR models into deployable ML APIs that integrate seamlessly with our business stack.

2. Statistical Rigor & Correlation Research