Daniel McEnnis
Grove City, Ohio, US
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
Discuss
Topics Followed
46
Academics
14179
Algorithms
925
Artificial intelligence
671
Big data
1326
C
1398
C++
13068
Cloud Services
222
Cloud-based Software Delivery
133
Customer Acquisition
1674
Databases
1464
Distributed Systems
1221
Fundraising
98630
Growth
68714
Hadoop
481
Investor relationships
152
Ip
1140
Java
15611
Licensing
936
Linux
13959
Machine learning
1182
Market entry
226
Multimedia
452
Music Industry
822
MusicTech
55
Mysql
13465
Natural language processing
227
Perl
554
Programming
1755
Python
18656
R
381
REST
671
Recommendation Engines
37
Recruiters
16
Recruiting
23049
SQL
14555
Scala
184
Scalability
1125
Signal Processing
194
Social Network Analysis
104
Social networking
51873
Software Engineering
26679
Software tools
89
Spark
70
Technical Recruiting
607
Web Development
62804
Web services
1401
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