William Gorfein — Data Scientist & Quantitative Trading Researcher

William Gorfein MSc

ABOUT ME

Quantitative researcher building predictive-signal pipelines for equities and options at Issachar Technologies, with prior systematic-trading research at ProActive Capital Partners. Research stack centers on causal inference, meta-labeling, and regime-switching models in Python — translating discretionary trading intuition into testable, automated workflows. Earlier, founded PeerLogix in 2014 and led it through a $5.1M IPO the following year — continuing as Founder & CEO today, with two USPTO patents covering household-level streaming-viewership measurement. MSc Applied Data Science, University of Michigan (3.96 GPA). Operator background informs how I scope research, ship infrastructure, and connect models to PnL.

Day-to-day focus: systematic strategy research at Issachar Technologies, LP. Continuing as Founder & CEO of PeerLogix and Founder & Lead Analyst at FiscalBeat.

CAREER HIGHLIGHTS

Building predictive-signal pipelines for equities and options at Issachar Technologies using causal inference, meta-labeling, and regime-switching techniques in Python — translating discretionary trading hypotheses into systematic, repeatable research workflows that connect quantitative evidence to live trading and risk decisions.

Took PeerLogix public in 2015 on a $5.1M raise, becoming one of the youngest founder-CEOs of a U.S. listed company at 28 — navigating SEC reporting, audit, and investor-relations standards across both public and private phases of the business.

Secured two patents with the US Patent & Trademark Office, enabling PeerLogix to track and collect data on streaming and downloading of viewer content, including movies, television series, and music.

Built two production market-research indices end-to-end at FiscalBeat: the Economic Strength Index (forecasting U.S. recessions ~6 months ahead from 10 composite datasets) and the Cyclical Strength Index (predicting 1-week S&P 500 volatility with R² > 0.7) — shipped to a 50,000+ daily subscriber base.

PROFESSIONAL EXPERIENCE

QUANTITATIVE DATA SCIENTIST / TRADING STRATEGIST

Issachar Technologies, LP

Oct 2024 – present

Develop predictive-signal pipelines for equities and options using Python, machine learning, causal inference, meta-labeling, and regime-switching techniques.
Build and refine proprietary quantitative trading models that convert complex market data into actionable signals for trade selection, timing, and risk evaluation.
Translate discretionary trading ideas into systematic research workflows, enabling market hypotheses to be tested, automated, and incorporated into repeatable analytical processes.
Analyze market, options, and macroeconomic datasets to evaluate signal reliability, identify regime-sensitive behavior, and support evidence-driven investment decisions.
Automate signal-generation and research workflows to improve speed, consistency, and operational efficiency across the trading research process.
Partner with fund leadership to interpret model outputs, refine strategy logic, and connect quantitative evidence to practical trading and risk-management decisions.

QUANTITATIVE TRADING ANALYST

ProActive Capital Partners, LP

Jan 2023 – Oct 2024

Applied statistical and machine-learning techniques to refine systematic algorithms, improving signal accuracy and Sharpe across equity strategies.
Partnered with the fund manager to translate discretionary trade ideas into automated execution logic, lifting consistency and freeing capacity for new research.
Conducted alpha research and synthesized market microstructure, options, and macroeconomic data into actionable, evidence-driven recommendations.
Migrated legacy manual trading workflows to a fully automated framework, reducing operational latency and standardizing PnL attribution across strategies.

FOUNDER & LEAD ANALYST

FiscalBeat.com

2023 – present

Pioneered an AI-driven platform for automated finance content generation, attracting over 50,000 daily email subscribers.
Created the Economic Strength Index (ESI) using AI and FRED data, providing powerful insights for recession forecasting.
Used the Economic Strength Index (ESI) to accurately forecast recessions six months prior to onset, utilizing a composite of ten economic datasets and machine learning - providing reliable, early warnings of economic downturns. ESI Website
Developed the Cyclical Strength Index (CSI), a critical tool for market timing and short-term trading strategy formulation.
Used the Cyclical Strength Index (CSI) to predict 1-week forward volatility in the S&P 500, achieving a notable R² score of over 0.7. This index accurately gauges the market's cyclical or defensive positioning, providing investors with key insights into overbought or oversold conditions during periods of extreme market sentiment, aiding in identifying optimal trade entry points and the potential end of deep sell-offs. CSI Website

FOUNDER & CEO

Peerlogix.com

June 2014 – present

Founded PeerLogix in 2014; led the company through a $5.1M IPO the following year and continued operating as Founder & CEO through public and now-private phases.
Built and patented (two USPTO grants) a household-level OTT viewership data platform now observing roughly 4–5% of global streaming across 100M+ households.
Established the company as a recognized data partner to Oracle, Salesforce, Experian, The Trade Desk, and Nielsen — cementing its role in cross-platform digital advertising.
Designed a viewership-forecasting model using machine learning to project subscriber growth and content performance, sold into hedge funds for entertainment-sector edge.
Pioneered a content-acquisition tool that informed strategic decisions at platforms including HBO Max and Pluto TV — and contributed to the strategic context around Viacom's 2019 acquisition of PlutoTV.
Hands-on operator experience with SEC filing requirements, GAAP financial reporting, and audit cycles for a publicly listed issuer.

SELECTED PROJECTS

Case studies from quantitative research, NLP, and streaming-data work — problem, approach, and result.

Equity Event Forecasting

Problem: Predict short-horizon equity events by fusing retail sentiment with options & short-sale flow.

Approach

NLP feature extraction over r/WallStreetBets posts (sentiment, topic, novelty)
Joined with equity options chains and FINRA short-sale data
Trained ML classifier; evaluated on out-of-sample event windows

Result: Demonstrated lift over price-only baselines on labeled event set (UMich capstone deliverable).

PythonPyTorchNLPFINRAOptions Data

Code →

GDP Prediction via FOMC NLP

Problem: Translate qualitative FOMC minutes into a quantitative input for U.S. GDP forecasting.

Approach

Topic modeling and sentiment scoring of FOMC minutes
Feature engineering aligned to release calendar
Hybrid econometric + machine-learning forecast

Result: Improved econometric forecast accuracy vs. macro-only baseline (UMich Milestone II).

PythonNLPEconometricsscikit-learn

CSI — Cyclical Strength Index

Problem: Build a market-cycle indicator that holds up under Black-Scholes-style assumptions.

Approach

Constructed CSI/ESI signal definitions from price-volatility geometry
Validated against option-implied baselines
Productized at FiscalBeat for daily subscriber consumption

Result: Predicts 1-week S&P 500 volatility with R² > 0.7; deployed in trading research workflows.

QuantVolatilityOptionsBlack-Scholes

Write-up →

PeerLogix Streaming Viewership Pipeline

Problem: Operationalize multi-platform streaming-viewership signals as a licensable data feed.

Approach

ETL across multi-platform viewership panels
Audience-segmentation features for ad-tech consumers
Bar-chart-race visualization for market education

Result: Integrated by Nexxen; covered by Next TV; observed across 4–5% of global streaming.

Streaming DataETLAdTech

Write-up →

TV & Movie Genre Classification

Problem: Auto-tag titles for PeerLogix metadata enrichment without manual curation.

Approach

Multimodal features (text synopsis + structured metadata)
Multi-label classifier with class-imbalance handling
NLP + logistic regression on plot descriptions

Result: Reduced manual tagging burden in PeerLogix catalog; improved partner ROI on enrichment.

PythonNLPClassification

Code →

PATENTS

US10402545B2

Dynamic Content Delivery Framework for Distributed Networks.

US9977877B2

Digital Content Rights Protection and User Tracking Methodology.

EDUCATION & TRAINING

Master of Science (MSc) in Applied Data Science | University of Michigan | GPA 3.96/4.0

(Advanced Machine Learning Techniques, Neural Networks & Deep Learning, Natural Language Processing, Big Data Analytics)

Key Projects (associated with master’s degree):

GDP Prediction Using FOMC Analysis (Milestone II Project)

Led a collaborative team project to analyze FOMC minutes for U.S. GDP prediction.
Utilized Natural Language Processing (NLP) techniques to extract economic sentiment and key topics, enhancing econometric forecasting accuracy (i.e., GDP prediction).

Equity Event Prediction Using Sentiment and Market Data Analysis (Capstone Project) - GitHub Repo

Developed an innovative model that predicts key equity market events by integrating sentiment analysis from Reddit’s WallStreetBets and critical financial data including equity options and FINRA short sales information.
Applied Natural Language Processing (NLP) and machine learning for comprehensive market analysis, contributing to more accurate trading strategies.