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DarienNouri/README.md

I'm a data scientist working on forecasting and alternative data. In summer 2023 I interned at Citadel, building KPI beat/miss signals from alternative data that went into a live systematic equity strategy. From 2023 to 2025 I ran PropertizeAI, a real-estate valuation startup.

I'm finishing an MS in Computer Science (Machine Learning) at Georgia Tech in December 2026, after a BA in Data Science and Computer Science from NYU in 2024.

Looking for full-time quant research or data science roles. Based in New York, open to relocating.

nouri.darien@gmail.com · LinkedIn

Kalshi temperature markets vs. a public forecast

kalshi-forecast-value is an out-of-sample test of whether prediction-market prices add information to a public weather forecast. Kalshi's NYC daily-high contracts are read as probabilities over temperature bins and compared with NOAA's point forecast, mapped to the same bins by a model fit only on earlier dates. Every price and forecast used was available before its checkpoint, 12 or 6 hours before the settlement day begins.

On 90 held-out days (April to June 2026), the market's Brier loss was 0.694 vs. 0.768 at 12 hours and 0.681 vs. 0.762 at 6 hours. Block-bootstrap intervals for both differences exclude zero. A blend weight fit on earlier dates went to its upper bound of 1: all market, no public forecast. The result is relative to that benchmark, for one station and a spring holdout.

Kalshi markets vs. a public forecast: mean multiclass Brier loss on 90 held-out dates by method, 12 and 6 hours before the settlement day; lower is better

Other projects

  • Bayesian lag selection: spike-and-slab selection over 54 candidate predictors (six NYC data series at lags of 0 to 8 months) for NYC home prices. 311 calls at a 2-month lag and evictions at 6- and 8-month lags had the highest inclusion probabilities.
  • City data and real estate: NYC building complaints, evictions and 100M+ Citi Bike trips as leading indicators for home prices and REIT ETFs, with a scraping, geocoding, MongoDB and Postgres pipeline. NYU research project with Prof. Anasse Bari.
  • Model complexity in pair-spread forecasting: NYU team project (2024) comparing linear, tree-ensemble and LSTM models on price and news-sentiment features for stock pairs.

Pinned Loading

  1. kalshi-forecast-value kalshi-forecast-value Public

    Kalshi NYC temperature markets vs. NOAA forecasts: market had lower Brier loss on 90 held-out days.

    Jupyter Notebook

  2. bayesian-factor-selection bayesian-factor-selection Public

    Spike-and-slab selection over 54 predictors (6 NYC series at lags 0 to 8 months) for home prices.

    Jupyter Notebook

  3. alt-data-real-estate-predictions alt-data-real-estate-predictions Public

    NYC building complaints, evictions and Citi Bike trips as leading signals for home prices and REITs.

    Jupyter Notebook

  4. ml-trading-complexity ml-trading-complexity Public

    Spread forecasts for S&P pairs: linear, tree and LSTM models on price and news-sentiment features. NYU team, 3 authors.

    Jupyter Notebook 7 2