Data Scientist - People Analytics (AutoPH Team)

Key responsibilities

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

• Cracking the Input/Output Code: - You will apply advanced statistical methods (correlation studies, causal inference, multivariate analysis among others) to prove (or disprove) which "effort" metrics actually move the needle on business outputs.
• Bias Mitigation: Use advanced normalization and cohort analysis to eliminate "noise" and systemic bias in performance grading, ensuring a level playing field for every employee.
• Data Integrity: Define the taxonomy for performance data. You’ll ensure that our data architecture is clean, scalable, and audit-ready.

3. Productized Analytics & Storytelling

• Insight Systems: Don’t just build "reports"—build products. Architect scalable, intuitive Power BI/Tableau dashboards that allow leaders to self-serve insights.
• Narrative Building: Translate high-dimensional math into low-friction business narratives for the C-suite. You are the bridge between raw data and strategic execution.

Skill and Experience

• The Toolkit: Solid foundation in Python and SQL and experience within the modern ML stack (Scikit-learn, TensorFlow/PyTorch) and BI tools (Power BI/Tableau).
• The Experience: 5+ years in Data Science, with at least 2-3 years preferably embedded in People Analytics or Talent Product environments. You understand the nuances of "human data."
• The "Product" Mindset: You’ve moved past simple data requests. You have experience identifying "pain points" in a system (like a legacy PMS) and engineering a technical solution to fix them.
  • Mathematical Grit: You are comfortable with complex variables, cohort normalization, and the challenge of quantifying the "unquantifiable" aspects of human performance.