Quantitative research · Data engineering · Analytics

Research and data systems for sharper decisions.

I’m Omar, a New York–based quantitative researcher and data professional who turns market signals and complex datasets into reliable systems, useful models, and clear decisions.

Selected work View resume

Selected work

Work that connects systems, models, and decisions.

A selection of end-to-end work across market intelligence, data engineering, analytics, and applied ML.

01

Data product · Market intelligence

Neighborhood Investment Intelligence

An evidence-first platform for screening neighborhoods, researching properties, and modeling real-estate investment decisions.

  • Built a reproducible public-data pipeline spanning demographics, labor, housing, infrastructure, risk, and regulatory evidence.
  • Designed an investor-focused application for market discovery, property underwriting, source review, and ongoing monitoring.
  • Python
  • React
  • TypeScript
  • DuckDB
View repository
02

Data engineering · Machine learning

Pokémon TCG Market Intelligence

A multi-source lakehouse that turns fragmented card and marketplace data into explainable valuation signals.

  • Built Bronze, Silver, and Gold Delta tables from Pokémon TCG and JustTCG APIs.
  • Combined rule-based scoring with a Random Forest baseline, confidence labels, and auditable reason codes.
  • Databricks
  • PySpark
  • Delta Lake
  • MLlib
View repository
03

Data engineering · Analytics

NBA Data Lakehouse

A reproducible pipeline for NBA games and betting-market data, designed for daily loads and historical backfills.

  • Landed API payloads as date-partitioned JSONL before loading raw Delta tables.
  • Modeled teams, dates, games, and bookmaker odds with normalized joins and validation notebooks.
  • Python
  • Databricks
  • Spark SQL
  • Delta Lake
View repository
04

Applied ML · Cloud analytics

Patient Length-of-Stay Prediction

An end-to-end forecasting workflow built to support hospital capacity planning and operational decisions.

  • Connected Snowflake feature engineering to Python preprocessing and model selection.
  • Compared regression candidates, wrote scores downstream, and designed drift-monitoring workflows.
  • Snowflake
  • SageMaker
  • XGBoost
  • Python
View repository

Experience

Market research grounded in data and business context.

Experience spanning digital-asset research, automated strategy development, customer analytics, experimentation, and ROI measurement.

2020 — 2025

Independent

Quantitative Researcher & Trader

Researched digital-asset markets by connecting on-chain activity, wallet behavior, narrative momentum, and sentiment to investment theses and risk decisions.

  • Built Python pipelines with pandas, Web3, and Etherscan APIs to detect on-chain events and support automated trade execution.
  • Developed a high-signal wallet network using historical P&L, position changes, and transaction behavior to surface real-time signals.

2018 — 2021

Omnicom Group — RAPP Worldwide

Experience Analyst

Translated customer, campaign, and website data into growth strategy, experimentation plans, and measurement frameworks for CRM programs.

  • Used R, Google Analytics, and Google Tag Manager to improve digital, email, and direct-mail performance, including 20%+ year-over-year interaction growth.
  • Built PostgreSQL and Tableau ROI models for a multi-million-dollar loyalty program and supported senior leadership on data-led new-business pitches.

Profile

Technical depth. Commercial context. Product thinking.

My work spans quantitative research, market intelligence, customer analytics, data engineering, and applied machine learning. As an independent researcher, I connected wallet behavior, alternative data, and market narratives to trading decisions. At RAPP Worldwide, I worked across CRM measurement, experimentation, customer behavior, and ROI modeling. Across each setting, I focus on turning messy data into reliable systems and clearer decisions.

Customer & marketing analyticsCommercial measurement

CRM analytics, experimentation, segmentation, customer behavior, and decision support.

Quantitative market researchSignals with context

On-chain activity, wallet analysis, narrative research, sentiment, and risk-aware decision support.

Data engineering & applied MLReliable analytical systems

Pipelines, lakehouse architecture, feature workflows, model evaluation, and monitoring.

How I work
  1. Frame the decision
  2. Build the system
  3. Improve through evidence

Foundation: B.S. Applied Mathematics · B.A. Economics, Stony Brook University.