Eric Wait, PhD
Technical Leader | Principal Data Scientist
Product Strategist
I bridge technical teams and product
vision to build systems that ship
With 21 years of military leadership, elite research experience, and startup product development, I bring a rare combination: deep technical expertise in GPU-accelerated ML systems and the ability to translate complex problems into executable strategies that align cross-functional teams.
What I Bring
Translation & Communication
Rare combination of deep technical expertise and exceptional people skills. I bridge technical teams and product management to accelerate development and improve team cohesion.
Product Vision to Reality
From "what if?" wonderment to deployed systems. I shape strategy, align stakeholders, and deliver production-ready solutions—90%+ reduction in analysis times, FDA-aligned systems.
Cross-Functional Leadership
21 years military leadership (2x Meritorious Service Medal). Communication under pressure, operational discipline, and building capability in others through empathetic mentorship.
How I Solve Problems
🎯 Outcome-Driven Discipline
I ensure teams build what's needed, not just what's possible. This means returning to "what does success look like?" at every decision point—when project pressures mount, when technically impressive alternatives emerge, when scope threatens to creep.
I've applied this discipline advising 170+ scientists, preventing wasted effort on experiments that couldn't answer their core questions.
Published framework: Journal of Cell Science (2020) - "Hypothesis-driven quantitative fluorescence microscopy"
🧠 Domain-Informed ML: "The Best Model is the One You Don't Need to Train"
By encoding physical constraints and domain knowledge directly into ML architectures, I build systems that are more efficient, interpretable, and robust than purely data-driven approaches.
Delivering Impact at Scale
Led cross-functional teams to production clinical tools with dramatic efficiency gains and multi-site reproducibility
Applied outcome-driven discipline enabling discoveries published in Nature and Nature Communications
Open-source GPU libraries globally adopted—still actively used because thoughtful design serves real workflows
Featured Work
Cancer Diagnostics Platform
Product leadership from "what if?" to production clinical tools. Cross-functional team alignment, FDA-aligned systems, 90%+ efficiency gains.
Production Leadership →
Hydra Image Processor
Open-source GPU library achieving 100x speedups. 12+ years continuous development, globally adopted by research institutions.
Building What Lasts →
Terabyte-Scale Imaging
Applied outcome-driven discipline advising 170+ scientists. Enabled discoveries in Nature and Nature Communications.
Research Impact →
What Makes Me Different
The Rare Combination: Technical Depth + Exceptional Communication
Most technical leaders come from one of two paths: deep technical background with limited people skills, or strong communication skills with surface-level technical understanding.
I bring both: PhD-level technical expertise in GPU-accelerated ML systems AND exceptional ability to translate complexity into clarity. This combination accelerates development, improves team cohesion, and ensures technical excellence serves business goals.
Diverse Leadership Background
21 years military service (Air National Guard, 2x Meritorious Service Medal) taught me communication under pressure and operational discipline. Elite research institutions honed problem-solving methodology and advising scientists across domains. Startup product leadership delivered production systems balancing innovation with execution discipline.
Learning to See Without the Camera
Professional photography training taught me pattern recognition that now informs data visualization, compositional discipline that ensures reproducible systems, and a fundamental truth: the best solution often requires subtracting, not adding—whether in photographs or ML architectures.
"The camera is an instrument that teaches people how to see without a camera." — Dorothea Lange
Technical Approach: Physics-Informed ML
For those interested in the technical details of how I build efficient ML systems
More Efficient
Encode physical constraints to reduce training data needs by 80%
More Interpretable
Models that follow known physical laws are easier to debug and trust
More Robust
Systems that respect physics generalize beyond training distribution
Concrete Examples:
- → Encoding optical constraints into imaging pipelines → 60% inference speedup
- → Using domain knowledge to reduce training data needs by 80%
- → Respecting physical limitations enables edge deployment
- → Custom CUDA kernels achieve 100x speedups over CPU implementations
Let's Build Something That Ships
If you need someone who can shape product vision, bridge technical and business teams, and deliver production ML systems—let's connect.