About

I am one of the co-founders and CTO of .txt. We raised $11.9 million in 2024 to improve the reliability of LLMs through structured generation. Before launching .txt in 2023, I spent a decade building and leading data science and engineering teams at companies like Ampersand and Normal Computing.

My technical specialty is operating at the intersection of data science, software development, and ML Engineering. Most of my work involves building, from scratch, end-to-end machine learning systems. Doing this requires both broad technical expertise and experience building the teams to support these complex systems in the long term.

Work Experience

Co-Founder and CTO - .txt - (2023 - Present)

Building .txt! We’re always looking for great people to hire. Don’t hesitate to reach out if you think there’s a fit.

VP of Engineering - Normal Computing - (Apr 2023 - Oct 2023)

Leading engineering at Normal Computing, a deep-tech AI startup focused on production-ready, reliable, generative AI workflows.

Head of Data Science & ML Engineering - AmpersandTV - (2018 - 2023)

• Built distributed Data Science and ML Engineering teams of 8 engineers, including lead developers of Aesara and BlackJAX
• Reduced median absolute error of Nielsen impressions forecasts by 40% through Bayesian modeling framework

President & Founder - Enplus Advisors - (2011 - 2023)

• Solving problems at the intersection of data science and software engineering
• Ranked Clutch.co #1 boutique Data Analytics and Big Data Firm and #9 overall in 2019

Quantitative Analyst - Geode Capital Management - (2007 - 2011)

• Researched, developed, and implemented alpha producing models
• Assisted with daily trading of $20M Global Relative Value Strategy (Currency, Commodities, Volatility)

Open Source

bootES: Calculate robust measures of effect sizes using the bootstrap.

backtest: The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera).

portfolio: Classes for analysing and implementing equity portfolios, including routines for generating tradelists and calculating exposures to user-specified risk factors.

Aesara: Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

outlines: Structured generation in Python

Programming with Data: Go from beginner to practitioner using Python and pandas to manipulate tabular data. Taught to 1,000s of students around the work and assumes no experience with pandas.

Publications

Jeffrey Enos, Daniel Gerlanc, Brandon Willard, Pierre-Yves Aquilanti, and Ala Abunijem. Bayesian ML Models at Scale with AWS Batch. AWS HPC Blog, June 14, 2022.

Kirby, K. N., & Gerlanc, D. (2017). Finding Bootstrap Confidence Intervals for Effect Sizes with BootES. APS Observer, 30(3).

Iyengar A, Paulus JK, Gerlanc DJ, Maron JL. Detection and Potential Utility of C-Reactive Protein (CRP) in Saliva of Neonates. Frontiers in Pediatrics, November 2014.

Daniel Gerlanc and Kris Kirby, bootES: An R Package for Bootstrap Confidence Intervals on Effect Sizes. Behavioral Research Methods, March 2013. (Preprint)

Kyle Campbell, Jeff Enos, Daniel Gerlanc, and David Kane. Backtests. R News, 7(1):36-41, April 2007.

Podcasts

• AWS HPC Tech Shorts - Bayesian models and half a million cores - what’re you waiting for?
• MLOps.community - Video
• The Accidental Engineer - Podcast
• The Solo Coder - Podcast
• ODSC AI X - Podcast

Live Teaching and Recorded Courses

• Programming with Data: Python and Pandas (Recorded Training). Pearson, 2020.
• Python and Dask: Scaling the Dataframe (Live Training on oreilly.com). Pearson, May 2020 – Present.
• Programming with Data: Advanced Python and Pandas (Live Training on oreilly.com). Pearson, May 2019 – Present.
• Programming with Data: Foundations of Python and Pandas (Live Training on oreilly.com). Pearson, September 2018 – Present.

Education

Williams College, B.A., 2007

• Majored in Comparative Literature
• Created an unofficial “Data Science” course of study by combining coursework in Computer Science, Statistics, and Economics