Daniel McEnnis

Grove City, Ohio, US

Researcher Consultant
Daniel's Skills
Technical
Product Management
Background

About Daniel

I crossed from academic data science in the music domain to commercial data science in 2011, previously working in music recommendation at scale and distributed digital signal procesing.

I worked at Gracenote for 2 years where I and a small team replaced over 50% of Gracenote's previous revenue with new products and enhancements of products outside Gracenote's core CDDB business.

For 5 months, I consulted with Rdio providing a number of research solutions including definitive track selection (including clustering of tracks at scale), implementing their new collaborative filtering engine, designed their automation pipeline for analysis and model evaluation to deployment, and several additional projects.

Recently, I have founded Research at Scale as a consulting company looking to fund data science tool development with consulting contracts, eventually morphing into providing consultant and outsourcing friendly tools for data science analysis as a product as a service.

Work Experience

CEO

Research at Scale

December 2014 - December 2016