Appen appoints former Pivotal Software and Astronomer SaaS executive Andrew Ettinger to lead worldwide sales
23 May 2023 - 12:34AM
Business Wire
Appen Limited (Appen) (ASX: APX) the leading training data for
AI company that makes trusted deep learning and generative AI work
better for everyone, today announced the appointment of Andrew
Ettinger as Chief Revenue Officer (CRO), effective 22 May 2023.
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Andrew Ettinger joins Appen Limited as
Chief Revenue Officer
For more than 25 years Appen has successfully helped some of the
largest big tech and enterprise customers adapt deep learning AI
and generative AI solutions. Andrew will direct Appen’s global
go-to-market sales and operations to meet our growth objectives.
Andrew brings over 25 years of leadership experience in growing
software and services companies from start-ups to scale-ups to a
successful IPO with scaled growth. Prior to joining Appen, Andrew
served as the Chief Revenue Officer at Astronomer (a Sutter Hill
Ventures backed company), where he grew the adoption of an
open-source data solution from 250k monthly downloads to over 15
million and increased revenue by 20x over his tenure. Previously,
Andrew served as a North American sales executive at Pivotal
Software, where he helped scale the business from US$0-$100 million
in annual recurring revenue ahead of Pivotal’s IPO, and up to
US$500 million during his seven year tenure.
Andrew will be based in Appen’s offices in North America.
Armughan Ahmad, CEO of Appen, commented:
“We are building a world-class sales organisation that works
together with our dedicated customer success and support teams to
help our customers deploy AI. As our CRO, Andrew will oversee and
further align the operations of these great teams and identify new
opportunities and partnerships to augment our growth. His extensive
experience in data, cloud native applications and overall digital
transformation of enterprise business functions will play a pivotal
role in helping Appen accelerate our go to market expansion and
revenue growth.”
The upcoming launch of Appen’s new large language model (LLM)
data products is designed to enable enterprises to utilise their AI
applications with confidence that their generative AI models are
trustworthy. This is one of the many initiatives that Andrew will
lead. The products offer a range of features that prioritise trust,
risk management, and assurance while also providing businesses with
complete visibility into the performance of their models.
A key factor in Andrew’s decision to join Appen is the
opportunity to enable a Safe and Trusted AI world that also
delivers on the promise of #AIforGood.
“In order for AI to run the world it must be safe and trusted by
everyone, in every place and at every moment. This is easy to say
and hard to do. Through Appen’s diverse crowd and human in the loop
offerings, I believe we are well positioned to ensure that AI can
be trusted and perform correctly. I am thrilled to join a team that
is trusted by some of the largest technology companies and
enterprise customers on the planet. I look forward to delivering on
the enormous potential of deep learning and generative AI,” says
Andrew.
About Appen
Appen is a global market leader in data for the AI Lifecycle.
With over 25 years of experience in data sourcing, data annotation,
and model evaluation by humans, we enable organisations to launch
the world’s most innovative artificial intelligence systems.
Our expertise includes a global crowd of more than 1 million
skilled contractors who speak over 235 languages, in over 70,000
locations and 170 countries, and the industry’s most advanced
AI-assisted data annotation platform. Our products and services
give leaders in technology, automotive, financial services, retail,
healthcare, and governments the confidence to launch world-class AI
products.
Founded in 1996, Appen has customers and offices globally.
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version on businesswire.com: https://www.businesswire.com/news/home/20230522005437/en/
For more information: Rosalie Duff Head of Investor
Relations +61 421 611 932 rduff@appen.com
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