Matthew is a data and infrastructure engineer focused on scaling reliable, cost-efficient machine-learning and analytics platforms. He has led data platform and governance efforts at Plaid and Metronome, supporting critical financial workflows and enterprise launches, and works with growth-stage companies to improve reliability, MLOps, and infrastructure efficiency. His work is grounded in hands-on system design and operational execution. Matthew holds a Bachelor’s degree in Mathematics from Bowdoin College and completed visiting studies at New York University.

Neal is a data scientist specializing in forecasting, optimization, and credit risk modeling. He has built and maintained machine learning systems for alternative lending, insurance, and large-scale optimization in both startup and research environments, including ZestFinance, System1, and Google. His work combines statistical rigor with hands-on data engineering and model deployment in production settings. Neal completed doctoral studies in Statistics at UCLA and holds a Master’s degree in Information Systems from UC Berkeley and a Bachelor’s degree in Computer Science from Washburn University.

Alex is a machine learning and data science leader with deep experience in credit, fraud, and enterprise ML platforms. He has built and operationalized automated training, validation, and monitoring systems at companies including Mission Lane, Capital One, and Persefoni, with direct ownership of high-impact credit decisioning models deployed at scale. His work sits at the intersection of modeling rigor, regulatory constraints, and production reliability. Alex holds a PhD in Mathematics from New York University and a Bachelor’s degree in Mathematics from MIT.

Tanya is an applied AI leader with extensive experience building and deploying large-scale machine learning and generative AI systems. She leads applied science teams at Amazon across Search and conversational AI, and previously built regulatory-grade and production ML systems at Moody’s Analytics, the Federal Reserve, and fintech startups. Her work emphasizes evaluation, safety, and the reliable translation of advanced research into customer-facing systems. Tanya holds a PhD in Electrical Engineering and Computer Science from UC Berkeley, along with graduate training in statistics and financial engineering.

Michael is a senior software and data engineer with deep foundations in formal systems and large-scale production software. He has spent several years building and maintaining complex systems at LinkedIn and leading engineering efforts in regulated technical domains, with a focus on correctness, clarity, and operational reliability. His background brings formal rigor to practical system design and implementation. Michael holds a PhD from UC Irvine and a Bachelor’s degree from Harvey Mudd College.

Howe works at the intersection of machine learning/ai systems, data engineering, and product execution. He has led data products supporting ML- driven and complex decision systems at Lyft and founded LLM-powered data platforms focused on real-time market intelligence and investment analytics. His work emphasizes translating complex data into automated, reliable operational decisions. Howe holds an MBA in Finance from Wharton and a Master's in Computer Science from Johns Hopkins.

DJ Rich works with clients to translate business constraints and objectives into algorithmic solutions and deliver them into production. Before co-founding True Theta, he spent five years at Lyft as a machine learning research scientist, developing pricing, forecasting, and planning algorithms. He also worked in the hedge fund industry modeling risk and credit portfolios. DJ holds a Master’s in Financial Engineering from UC Berkeley.