Greater Boston
I build AI and data platforms that make it into production — and the teams and practices that sustain them. I was the founding member of the Data & AI practice at Thoughtworks where I served as the principal technical voice on large-scale AI and data platform programs across finance, healthcare, manufacturing, robotics, energy, and government. I co-created CD4ML (Continuous Delivery for Machine Learning), a framework for bringing software engineering discipline to ML delivery that has been widely adopted and referenced across the industry. My view on AI hasn't changed much since then: the teams that succeed are the ones that treat AI as a software engineering problem first. The ones that don't tend to have impressive demos and struggling production systems. Before industry I spent eight years as a research scientist in astrophysics and cosmology at NASA/JPL, Princeton, Fermilab, and Northwestern — building large-scale data pipelines and statistical ML methods for projects like the Sloan Digital Sky Survey. My work is cited 37,000+ times with an H-index of 68. It gave me a foundation in rigorous quantitative thinking that I've carried into everything since. Most recently I've been Chief Data & AI Scientist at Navalia, leading data platform delivery for robotics and hospitality technology clients and advising executive leadership on AI architecture and strategy. I write regularly on AI, data platforms, agentic systems, and the gap between AI hype and production reality. Author of recent book: Navalia Pulse Vol 1 : Building Evolutionary Platforms Infra, Data Platforms and Data Mesh in Practice I'm selectively open to senior AI leadership roles — Head of AI, VP Applied AI, CDAO/CAIO — where the work is serious, the scope is real, and AI is central to what the company is building.
Senior technical and strategic leader responsible for AI and data platform architecture across enterprise client engagements. Led end-to-end design and delivery of a production data and AI platform for Path Robotics, enabling real-time operational intelligence and ML-driven decision systems across factory operations — covering data ingestion, domain-oriented modeling, ML integration, and production deployment. Advises executive leadership on AI roadmaps, governance models, and data mesh–oriented platform operating structures across multiple sectors.
Founding technical voice of Thoughtworks' global Data & AI practice — brought in as the first data scientist and served for 12 years as the company's deepest AI and data science authority, setting technical direction, defining methodology, and establishing the intellectual foundation the practice was built on. Principal architect and technical lead on large-scale AI and data platform programs across finance, healthcare, manufacturing, energy, and government — responsible for the hardest modeling and architecture decisions on each engagement, often in domains with no established playbook. Originated and co-developed CD4ML (Continuous Delivery for Machine Learning) — a framework that emerged from direct observation of how ML delivery was failing in practice, now referenced and adopted across the industry.
Developed techniques for cosmological parameters extraction from combined astronomical data sets. Developed algorithms for compressed communication with satellites.
Conducted research with Professor Melville Ulmer on weak gravitational lensing to map dark matter distribution in galaxy clusters. Image processing algorithms and statistical estimation. Led and mentored team of students in astrophysics.
Designed and optimized future NASA space missions for cosmological research. Built data analysis pipelines for the Hubble Space Telescope. Developed an astronomical image simulation code. Lead scientific research on the Sloan Digital Sky Survey.