Data Scientist - People Analytics (AutoPH Team) at HighRadius

<h4><strong>About HighRadius!</strong></h4> <p>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 &amp; Reconciliation, Consolidation &amp; 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.&nbsp;</p> <h4><strong>Job summary</strong></h4> <p>We are redefining how performance is measured and rewarded. As a Data Scientist within the <strong>AutoPH Strategy Team</strong>, you won’t just be analyzing data—you will be architecting the algo that drives our next-generation Performance Management System.&nbsp;</p> <p>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.</p> <h4><strong>Key responsibilities</strong></h4> <h4><strong>1. Algorithm Engineering &amp; PMS Innovation</strong></h4> <ul> <li><strong>The AutoPH Engine:</strong> 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.</li> <li><strong>Model Evolution:</strong> You will critically evaluate our "AutoPH" logic and identify where our measurement systems lack precision. You will&nbsp; 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.&nbsp;</li> <li><strong>Predictive talent strategy:</strong> 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.</li> <li><strong>From Concept to Code:</strong> Develop Python scripts to transition theoretical HR models into deployable ML APIs that integrate seamlessly with our business stack.</li> </ul> <h4><strong>2. Statistical Rigor &amp; Correlation Research</strong></h4> <ul> <li><strong>Cracking the Input/Output Code:</strong> - 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.</li> <li><strong>Bias Mitigation:</strong> Use advanced normalization and cohort analysis to eliminate "noise" and systemic bias in performance grading, ensuring a level playing field for every employee.</li> <li><strong>Data Integrity:</strong> Define the taxonomy for performance data. You’ll ensure that our data architecture is clean, scalable, and audit-ready.</li> </ul> <h4><strong>3. Productized Analytics &amp; Storytelling</strong></h4> <ul> <li><strong>Insight Systems:</strong> Don’t just build "reports"—build products. Architect scalable, intuitive Power BI/Tableau dashboards that allow leaders to self-serve insights.</li> <li><strong>Narrative Building:</strong> Translate high-dimensional math into low-friction business narratives for the C-suite. You are the bridge between raw data and strategic execution.</li> </ul> <h4><strong>Skill and Experience</strong></h4> <ul> <li><strong>The Toolkit:</strong> Solid foundation in <strong>Python</strong> and <strong>SQL</strong> and experience within the modern ML stack (Scikit-learn, TensorFlow/PyTorch) and BI tools (Power BI/Tableau).</li> <li><strong>The Experience:</strong> 5+ years in Data Science, with at least <strong>2-3 years </strong>preferably<strong> embedded in People Analytics</strong> or Talent Product environments. You understand the nuances of "human data."</li> <li><strong>The "Product" Mindset:</strong> 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.</li> <li><strong>Mathematical Grit:</strong> You are comfortable with complex variables, cohort normalization, and the challenge of quantifying the "unquantifiable" aspects of human performance.</li> </ul> <h4><strong>Why This Role?</strong></h4> <ul> <li><strong>High Visibility:</strong> This isn't a back-office HR role. You are building a core piece of our company’s operating system under a CEO-led initiative.</li> <li><strong>Complexity:</strong> You’ll be dealing with one of the hardest data problems: predicting and optimizing human behavior at scale.</li> <li><strong>Autonomy:</strong> We value "scientific objectivity." You are expected to challenge the status quo if the data suggests a better way forward.</li> </ul> <p><br><br></p>