Quantifying natural-hazard and climate risk and turning it into financial loss numbers.

I'm a Statistics graduate moving into catastrophe and climate risk modeling. I'm currently doing an MSc in Atmospheric Sciences at NIT Rourkela, which gives me the hazard-science side to pair with the statistics and economics I already work in.

The short version of what I'm after: a meteorologist can model the cyclone but can't price the loss; a quant can price the loss but can't model the cyclone. I'm building toward doing both - the hazard ×ばつ exposure ×ばつ vulnerability → loss chain that insurers, reinsurers, and climate-risk teams actually use.

• Statistics & extreme-value theory - my core. Risk is a tail-probability problem, so this is the part I lean on most.
• Economics & finance - my minor. Turning a hazard into a loss number, a premium, a capital charge.
• Hazard science - from the MSc (tropical cyclones, floods, extreme heat, climate scenarios).
• Python / geospatial / ML - from coursework, a research internship in optimization, and the catastrophe modeling work below.

odisha-cyclone-risk - end-to-end catastrophe risk model for Bay of Bengal tropical cyclones over the coastal Odisha belt. Full hazard ×ばつ exposure ×ばつ vulnerability → loss chain in CLIMADA: 2,754-event stochastic catalogue validated against Cyclone Fani and Phailin, LitPop exposure, an OSDMA-derived vulnerability curve, OEP/AEP curves from a 100,000-year Year Loss Table, and CAT XL layer pricing.

Headline finding: vulnerability specification drives an ×ばつ spread in average annual loss - larger than climate intensification and exposure uncertainty combined - and propagates directly into reinsurance pricing. GPD tail extrapolation was tested and rejected as unsupported by the data.

lasalgaon-onion-dss - a decision-support system modeling price-crash risk for a commodity market, combining statistics with economic reasoning. Closest in spirit to the tail-risk / loss modeling above.

uidai-operational-dashboard - an operational analytics dashboard modeling district-level stress from real administrative data.

(Other repositories include a research internship in combinatorial optimization - Python + Gurobi - where the focus was rigorous, honestly-validated results.)

I'm early on this path, so I'd rather be honest about what's finished and what isn't:

• An exposure data-quality and portfolio accumulation engine - SQL-based validation, geocoding, anomaly detection and accumulation reporting, mirroring the daily work of an exposure analyst.
• A rapid event-response loss estimator - API-driven, producing a same-day loss estimate and event bulletin when a storm forms in the Bay of Bengal.
• OasisLMF - the open catastrophe-modeling framework, as a complement to CLIMADA.
• ML for Earth - applying machine learning to hazard problems, including the current generation of ML weather models.

Python (pandas, numpy, scipy, scikit-learn) · CLIMADA · statistics & extreme-value theory · geospatial Python (geopandas, xarray, shapely, pyproj) · optimization (Gurobi) · learning: OasisLMF · QGIS · SQL · PyTorch

Building consistently toward the intersection of climate science and financial risk. Open to conversations, collaborations, and pointers from anyone working in cat modeling or climate risk.