■FIG. B · Door B · Credit risk, market analytics, lender research
Markets & Risk
This practice is held to a stricter rule than most portfolios allow: every number must be reproducible from committed artifacts. The scorecard below runs the repository’s own lookup table and policy YAML; the backtest charts were produced by running the repository’s own engine over its own data lake; the lender leaderboard is extracted data, not a retyped claim. If a number can’t be traced, it isn’t here.
■ FIG. B.1
Lending Lifecycle Platform
300k+ Home Credit applications into a WoE scorecard, a YAML policy engine with adverse-action codes, an auditable decision API, and a simulated 500k-loan book.
KS 0.40 · GINI 0.52 · PSI MONITORED
■ FIG. B.2
Market-Data Ops & Portfolio Analytics
A cron-fed Parquet lake of NSE bhavcopies, VIX, NAVs and FX — reconciliation-grade — with VaR, drawdowns and a bootstrap-tested momentum backtest.
SHARPE 0.93 (RUN) · 2018–25
■ FIG. B.3
Listed-Lender Equity Research Workbench
Twelve listed lenders' disclosures extracted into source-cited ratio families, driver-based models, and timestamped initiation notes tracked against Nifty Bank.
12 LENDERS · COMPOSITE QUALITY RANKING
■ FIG. B.4
Banking-Documents GenAI Workbench
Bank-statement parsing into FOIR-grade features, regex-plus-Presidio PII masking you can test live here, and cited RAG over RBI Master Directions.
93% FIELD ACCURACY · 94% CITATION FIDELITY
■FIG. B·W · The desk months — analytics lens
Work, read as facets
Mar – Apr 2026 · Remote
Financial Analyst Intern — AQUA Pvt. Ltd.
Two months on a trading desk, doing the analytics the desk actually runs on. Intraday OHLCV across 40 NSE scrips — 90 days of it, in Python and SQL — flagged trend breaks and volatility regimes. Nine mean-reversion and momentum rules were backtested over two years of OHLCV; the best carried a post-cost Sharpe of 1.6 at 8% maximum drawdown. Daily derivatives screens over 180 NSE symbols — OI build-up, PCR, volume spikes — fed 8–10 setups a day to the intraday desk.
Risk sat beside return: a Python risk sheet computed daily VaR, beta and stop-loss levels for a 20-name book and escalated 3 limit breaches. Hypothesis-driven experiments on signal thresholds, run out-of-sample, lifted the strategy hit rate from 52% to 57% pre-rollout. Weekly P&L attribution across three books dropped from four hours of manual compile to 20 minutes, and the desk MIS — 15 KPIs: P&L, hit rate, Sharpe, drawdown — became automated EOD dashboards.
The fundamental side ran in parallel: 3-statement and DCF-comps models for 14 NSE-listed names, six investment notes, and a 5-stock long-term book shaped by them.
Jun – Jul 2026 · New Delhi
Analytics facet — Founder's Office (Software & Automation) Intern — The Rocket Media
The same analytics habit, pointed at content. A Python ETL unified Substack, three YouTube channels and podcast stats into one reporting layer feeding a weekly Looker Studio dashboard — views, watch-time, retention, CTR. The ML reel-selection funnel was audited in SQL: 15% conversion (11 of 75 candidates), and a failure-mode Pareto retuned the prompt thresholds. Keyword and competitor search analysis guided the SEO rebuild of the 98-page sitemap.
The first practice lives behind Door A — Engineering & AI. The person behind both is in About.