Matt Turk

Senior Data Scientist (AI Solutions Architecture)

2024

We are building a product to use AI to increase the value of your data automatically. It’s already the world’s most popular data-centric AI software (built on the foundation of confident learning pioneered at Massachusetts Institute of Technology by the founders during their PhDs). Cleanlab (https://cleanlab.ai) is the industry’s first no-code data curation platform that takes datasets across all modalities (text, image, tabular, voice) and automatically labels datasets, diagnoses issues, and provides complete characterization of issues automatically using novel algorithms built on confident learning.

Technical Advisor

2023

Advising on AI use cases and implementing large-language model use cases for workplace collaboration

Senior Applied Scientist & Investor

2022 - 2024

I focused on end-to-end quantitative investment analysis and alpha generation (with a particular focus on researching, building, and productionizing machine learning models to rank & value high-quality startups), alternative data analysis, thesis development, diligence, evaluating seed opportunities, and balancing humans and machines in capital allocation decisions. I've utilized state-of-the-art LLMs for thesis/compset generation, helped build an AI agent based due diligence system for analyzing data rooms, and experimented with many other foundation model use cases. As an investor, I also took direct ownership in these decisions. As a firm we focus on both early stage and late stage investments (For context, I try to bring significant expertise to analyzing both AI & Web3/Crypto investment opportunities). I've personally led the deal process for 5 seed stage companies. Goodwater Capital is dedicated to empowering exceptional entrepreneurs who are changing the world through consumer tech and is the largest consumer-tech focused venture capital firm in the world, managing over $5B of assets and serving over 600 portfolio companies in 30+ countries around the world. Goodwater and its partners have been honored to invest in and help build successful companies including Facebook, Twitter, Kakao, Chegg, Coupang, Weee!, Greenlight Financial, Monzo, Zumper, Stash, Toss, musical.ly/TikTok, Daangn Market, Xendit, Spotify, EverlyWell, and Getir. Our global family of companies provide innovative technology products that address the daily needs of consumers — housing, health care, food, financial services, transportation, education, and entertainment. We have funded 14 unicorns ($1B+) globally in 7 years from early-stage onward.

Senior Quantitative Researcher (Applied Scientist)

2021 - 2022

Building the quant layer of a crypto-based open financial system. Working on the quant research team founded by Yao Ma. We cover crypto in both CeFi (centralized finance) and DeFi (decentralized finance) We build cutting edge crypto-native quantitative models as well as data science and machine learning models to support Coinbase’s strategic initiative: Coinbase Prime, institutional trading/research and investment, financing, derivative pricing, crypto data insight, credit modeling, algo execution, and large scale crypto investment portfolio management. In DeFi we cover quantitative protocol research in decentralized exchanges (DEX), borrow/lend platforms, DEX aggregators, insurance platforms, etc. Some of the things I worked on include: a portfolio allocation model to analyze how BTC exposure affects risk-adjusted returns on a portfolio of stocks/bonds, a credit optimization algorithm for our institutional business, researching quantitative insights/predictive modelling on stablecoins and DeFi yield factors, and a real-time PnL dashboard for institutional trading Some of the content I co-authored: https://medium.com/the-coinbase-blog/part-1-quantitative-crypto-insight-stablecoins-and-risk-free-rate-9c2e34d7fffc https://www.coinbase.com/blog/part-2-quantitative-crypto-insight-stablecoins-and-unstable-yield?source=rss----c114225aeaf7---4

Quant Associate/ML Engineer

2020 - 2021

Central ML modeling team founded by Professor Charles Elkan Our team creates/improves quantitative models for revenue generating businesses of the firm. As examples, we work on models with Equities strats/trading teams and FICC systematic trading teams. My role involves both research and productionizing the models. We were granted a patent for one hedging model I co-led the development of.

Quant Analyst/ML Engineer

2018 - 2020

Undergraduate Research Assistant

2018 - 2018

Working on Spiking Neural Networks (FORCE Learning) for Brain-Machine Interfaces/Neuromorphic hardware

IBD Strats (Quant) Summer Analyst

2017 - 2017

Worked with R on a quant research project involving optimizing call option spread model predictions through machine learning techniques Worked with Scala and Apache Spark on making a relational database more scalable and accessible

Undergraduate Researcher

2017 - 2017

Research lab focused on developing real-time data processing technologies to securely make intelligent decisions ● Developed a machine learning model that can learn and utilize non-stationary time series data ● Took a deep reinforcement learning approach, using OpenAI’s RLLab by creating our own environment and ran our data using Trust Region Policy Optimization (TRPO) and a categorical Multi-Layer Perceptron (MLP) policy ● Executed a pairs trading strategy on real-world cointegrated stock price data ● Utilized parameters that reflect real-world portfolio assets with the real price data Working under the supervision of professor Joseph Gonzalez and graduate student François Belletti

Teaching Assistant

2017 - 2017

Teaching Assistant for iOS Programming Class (80+ students) started by Professor Dan Garcia. ● Helped with teaching material to students, guiding students through labs, and grading their homework/labs/projects. Specifically by the end of the semester, students learned a multitude of pragmatic iOS development skills and created a variety of apps (e.g. Utility, Game, Social Network) from scratch, including one entirely of their own idea/design.

Software Engineering Intern

2016 - 2016

● Worked on an AI Facebook messenger bot for Salesforce that serves as a sales assistant for salesmen and their clients ●Trained the bot to understand natural language, classify client data to optimize sales opportunities, and wrote front-end and back-end functionality ●Wrote end-to-end tests and developed an understanding in creating verbose test suites