Many companies rich in valuable data know they "need AI" but struggle with: What's actually possible vs. hype? Where to start? How to build internal capabilities? This often results in initiatives that:
- miss key opportunities,
- lack measurements to evaluate and improve system effectiveness, and
- are abandoned when they fail to meet unrealistic expectations.
I bridge that gap , combining deep technical expertise (PhD + 6 years at Voleon quant fund) with hands-on business application across finance, healthcare, and insurtech.
Recent examples:
- Helped an analytics company starting agentic development identify usecases, define an architecture, and find the relevant data to de-identify to balance regulatory constraints and functional goals.
- Helped a first-in-company predictive analytics effort refine Product insights into measurable scoreboards for data scientists to target, identified crucial historical data they weren't using, and drove the redesign of their data and model pipelines to increase model development velocity.
I am looking for companies ready to back their AI experiments with systematic capability building, who realize they have unknown unknowns, and looking for a fractional AI executive to build addressing them into company strategy, work plans, staffing and support for managers.
My areas of practice include finance, insuretech, healthcare (logistic and diagnostic), document management, applying a broad set of techniques acquired over a career spanning academic research, systems and core IP development for startups, algo-trading, government systems, enterprise and SaaS.
If you are ML/AI adjacent (data engineering, analytics-oriented data science, product/infra sides of AI), I am also interested in channel and other partnerships.
I am happy to chat, so feel free to reach out at danielv@nirinor.ai, or comment below!
Daniel Vainsencher
Nirinor, LLC