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Carolyn Mooney
Co-Founder + CEO @ Nextmv | DecisionOps+AI
Carolyn Mooney is the Co-Founder and CEO of Nextmv, a DecisionOps platform that accelerates optimization AI teams.24 With a background in systems engineering, Mooney has extensive experience in decision engineering and optimization.3
Career Highlights
- Nextmv: Co-founded in August 2019, where she currently serves as CEO.3
- Grubhub: Worked as Lead Systems Engineer in Decision Engineering (Feb 2018 - Jul 2019) and Senior Analyst in Decision Engineering (Feb 2017 - Feb 2018).3
- Lockheed Martin: Started her career working on ballistic missile and radar systems.6
- Zoomer: Analyzed simulations related to pizza delivery.5
Expertise
Mooney specializes in leveraging analysis, technical expertise, and stakeholder management to make complex concepts accessible and actionable for customers. Her work focuses on decision automation, optimization, and simulation technologies.6
Additional Roles
- Cherry Hill Volleyball Club (CHVBC): Head Coach since December 2010, where she applies her analytical skills to sports coaching.3
Education and Skills
While specific educational details are not provided, Mooney's background in systems engineering and her roles at various technology companies demonstrate her expertise in decision engineering, optimization, and automation.36
Company Leadership
Under Mooney's leadership, Nextmv has secured significant funding, including an $8 million Series A round led by FirstMark.12 The company aims to make optimization and simulation technologies accessible to developers, moving beyond traditional academic and defense contractor applications.6
Highlights
As modelers, we often think about the solving the problem - building routes, schedules, prices and assignments for our businesses. But too often it's difficult to integrate these decisions into our software systems in a way that helps the business scale and continuously improve.
That's why we are so opinionated about how to hook up #decisionscience models to infrastructure, scale solves in parallel, report solutions, and share results or experiments. I'm by no means a hardcore developer, but in this video I turn a sample-tsp #ortools #python model into a fully scalable API in under 10 minutes, complete with run stats and history access.
Modeling is hard enough, operationalizing it shouldn't be.
Build more decision models, not more decision tools ✌
#optimization #decisionops #orms #datascience
We’ve been talking a lot internally about MLOps and DecisionOps — how they’re similar, how they’re different, how they fit together.
I think there are key considerations are: ⚙ API complexity, 👩💼 business operator interactions and approvals, 🔬 online and offline testing workflows, 🏘 where logic lives (data side or model side).
Have you tried to do both? What tools have you used? What’s been your experience?
#datascience #orms #mlops #decisionops
