Verified Expert in Engineering
Generative Pre-trained Transformers (GPT) Developer
Tal is a Google developer with expertise in machine learning and a former NLP researcher at Citi. He is the founder and CTO of LightTag, a profitable NLP SaaS platform. His experience spans ML, ops, and human-machine interfaces. The solutions he's put in production include language-based compliance monitoring systems, high-frequency trading systems trading hundreds of millions a day, and NLP-based alternative data offerings for competitive intelligence and financial analysis.
The most amazing...
...thing I've built is a patented system for analyzing trader behavior based on the behavioral finance literature.
Founder, CEO, CTO
- Created a SaaS business for NLP annotation with customers including Viasat, Microsoft, and Pitchbook.
- Deployed a language-agnostic machine learning model that correctly generates 70% of entity annotations on the platform.
- Built a multi-tenant SaaS supporting thousands of tenants while maintaining strong guarantees on tenant data isolation and low infrastructure expenses.
- Invented and implemented a patent-pending interface for drag and drop relationship annotation supporting constituency and dependency grammar.
- Conducted customer interviews and implemented findings to increase conversions, retention rates, and customer delight.
- Designed and deployed deep NLP models that can adapt to customer data without incurring significant compute costs.
- Applied behavioral finance theory to create a patented system for detecting bias in credit trader behavior.
- Used rule-based and deep learning NLP to create multilingual compliance and CRM solutions for sell-side credit and rates trading.
- Reduced labor costs and turnaround time for institutional loan origination by developing ML-based document classification, routing, and extraction systems.
- Grew the engineering team from a team of one to a cohesive and productive team of 12.
- Reduced turnaround time on POCs from three weeks on average to less than 48 hours by making core data assets accessible to the business side.
- Led the technological and product shift of the company from a $0 revenue consumer-facing service to a multi-million dollar alternative data provider.
- Maintained an acceptable infrastructure cost as we grew our data processing scale 1,000X.
- Increased return on data annotation costs by developing a "human-friendly" domain-specific language for semi-structured text analytics.
- Drove data acquisition throughput by deploying a terabyte-scale Elasticsearch cluster and designing a custom interface to find "needles in haystacks."
Fluent Trade Technologies
- Deployed high-frequency algorithmic trading systems capable of trading hundreds of millions in notional volume a day.
- Implemented ML algorithms with single-digit millisecond latency to maintain an edge in HFT.
- Contributed to API design, usability testing, and QA as the company expanded into HFT PaaS offerings.
- Liaised between the research team, engineering, and senior management and helped frame objectives and challenges in an accessible form to each group.
- Designed, developed, and deployed a multi-equity long/short algorithmic trading system in C++.
- Implemented a multi-exchange and multi-threaded order management system.
- Developed backtesting infrastructure and data warehousing for equities data.
LightTag - Text Annotation SaaShttp://www.lighttag.io
I built LightTag because I needed it and turned it into a profitable business through a combination of ML and UX.
YLabel - Serverless, In-Browser Full Text Search and Annotationhttps://github.com/LightTag/ylabel
Dense Continuous Sentences - NLP Variational Autoencoder Using Densenethttps://github.com/talolard/DenseContinuousSentances
Article - Convolutional Methods For Texthttps://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f
Article - How To Label Datahttps://www.lighttag.io/how-to-label-data/
Introductory Course To NLPhttps://github.com/LightTag/NLPCourse
RLStocks - Real Time Portfolio Rebalancing with Transaction Costs Solved with Reinforcement Learninghttps://github.com/talolard/rlstocks
After a foray into modern methods, I focused on a paper from the early '90s (Learning to Trade via Direct Reinforcement by Moody) that offers a much more domain focused approach to policy gradient algorithms.
PyTorch, TensorFlow, React, Pandas, Scikit-learn
MetaTrader, MetaTrader 4, Docker, Amazon Web Services (AWS)
Data Analysis, Data Analytics, Natural Language Processing (NLP), Regular Expressions, Text Mining, Deep Learning, Machine Learning, GPT, Generative Pre-trained Transformers (GPT), Statistics, FIX Protocol, Trading Applications, Forex Trading
PostgreSQL, Redis, Elasticsearch
Bachelor of Science Degree in Mathametics
Tel Aviv University - Tel Aviv, Israel