Work

What I do

I help companies harness the power of math (predictive models, graph algorithms, optimization, experiments, etc.) to tame thorny, open-ended problems.

I work with data scientists and engineers to inject algorithmic smarts directly into core business processes. I started out doing econometric modeling in an academic research context and, over time, have focused increasingly on software engineering (a weak spot for most data science teams housed outside of IT).

My competitive advantage is being a decent engineer for someone with an algorithms, economics, and finance background. That cross-domain focus and appreciation aids in working with cross-functional teams to deliver solutions to complex problems.

Where I am now

I'm looking for my next role. After being laid off, I took the opportunity to focus on family and personal priorities. Now it's time to get back to work, and I'm itching to jump back in.

Where I want to be

• Developing core business functions. (Folks hear "data science" and assume I focus on analytics. I appreciate analytics as a tool to guide efforts, not as an end in itself.)
• Close enough to customers to understand them.

• With a collaborative team of talented people who take pride in their work and strive for balance:

  • Moving fast while leaving a solid foundation to build on.
  • Attending to metrics without supplanting intuition and vision.
  • Unleashing individuals while maintaining alignment.

What I bring to the table

Broad data science, engineering, and product experience:

• Across domains: Predicting demand and optimizing pricing of goods and services, optimizing transportation networks, recommender systems, fraud detection, modeling and trading financial derivatives, designing and analyzing experiments in industry and academia.
• Roles: As an individual contributor, team lead, and manager — proving the value of new data science teams, working with customers and stakeholders to find product market fit, building trust and consensus across teams, helping teammates refine and sell their work, hiring/onboarding/coaching new teammates, managing outside consultants and contractors.
• Engineering disciplines: Building and iterating on prototypes (backend and frontend), scaling and hardening proven prototypes into business critical services, refining unwieldy systems.
• Modeling disciplines: Machine learning, mathematical optimization, network algorithms, exploratory analysis, statistical modeling, design of experiments.
• And technologies: Python, JavaScript, Julia, SQL, HTML, React, Redis, Kafka, Postgres, etc.