Gunjan Kuhikar

Demand Forecasting & Automation Pipeline (2025) Built hybrid SARIMAX, XgBoost and Ridge Regression models for monthly forecasting across 40+ products, reducing MAPE by ∼23% versus legacy baselines. Engineered preprocessing with MAD-based outlier correction, 6,000+ FRED/Census indicators, lag features, month dummies and feature standardization.

Data Scientist at MATRiX Analytics Corporation (Domain of Arimax Global Corporation) (2023-08 – Present)

• Demand Forecasting & Automation Pipeline (2025)
• Built hybrid SARIMAX, XgBoost and Ridge Regression models for monthly forecasting across 40+ products, reducing MAPE by ∼23% versus legacy baselines.
• Engineered preprocessing with MAD-based outlier correction, 6,000+ FRED/Census indicators, lag features, month dummies and feature standardization.
• Improved inventory planning, reducing stockouts by ∼18% and over-ordering by ∼12%, delivering approximately $0.5M annual cost savings.
• Built an automated validation template across 6 countries and 40+ products, monitoring Actual-vs-Forecast variance, buffer breaches, PSI and historical trends; delivered weekly Monday exception reports for stakeholder action.
• Credit Scorecard Replication & Audit | Santander Bank
• Replicated and audited auto-loan credit risk scorecards, creating 7-module Excel audit packs covering performance, PSI, characteristics and multicollinearity.
• Validated a 24-month PD scorecard for used-auto loans, achieving stronger OOT discrimination with KS 57.3% vs. 47.7% and AUC 84.3% vs. 78.1%.
• Flagged calibration and population drift with ATM of 131.5% vs. 104.3% and PSI of 0.141 vs. 0.054; max correlation of 0.215 showed no multicollinearity concern.
• SignalScope AI — Agentic Financial-News Intelligence & Risk Monitoring Platform
• Built an on-premises financial RAG platform combining fine-tuned FinBERT, hybrid retrieval and a local LLM, converting 5,000+ news articles into source-cited reports and 24-month historical timelines.
• Engineered a 4-agent LangGraph pipeline for retrieval, materiality, verification and reporting, achieving 0.88 RAGAS faithfulness, 0.82 context precision and 93% citation correctness.
• Developed confidence scoring, contradiction and citation checks, flagging 18% low-confidence reports for review and reducing estimated monitoring effort by ~60%.
• Yafera — Enterprise Credit Risk Scorecard Platform
• Developed Python web scrapers using Selenium, Requests and BeautifulSoup to extract and standardize business data from 5+ public sources (SBA, SEC, GSA, DOL and OpenCorporates), enriching enterprise records for downstream credit-risk scorecard development.
• Engineered 60+ model-ready features from 30+ merchant and external attributes, including growth, ratio, rolling-window, seasonal and risk indicators to support downstream credit-risk scorecard modelling.

Post Graduation – IIT Bombay (2023)

Graduation – RAIT (2020)