Aporia, the end-to-end observability platform for machine learning, announced that experienced executive Tim Tyrrell has joined the company as Vice President of Sales. The former IBM sales leader brings a proven track record of building high-performance go-to-market teams that accelerate emerging data science and machine learning technology adoption.
“AI is now the norm, and with that comes new challenges,” said Tim Tyrrell. “I’ve seen how companies of all sizes struggle to gain visibility into their models to see where they’ve gone wrong and correct them. Aporia has impressed me with its machine learning observability platform that allows businesses to trust their AI. I’m excited to be part of this impressive team that has accomplished so much to illuminate the black box of AI and empower data scientists, ML engineers, and executives alike to manage and oversee their AI with confidence.”
Tim comes to Aporia with over two decades of sales leadership experience. He led a key segment of North American sales organization at IBM for all SPSS statistical software-related products and services before building out successful sales teams at a handful of notable enterprise AI, data science and database startups, including Domino Data Lab.
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“Tim is exactly the right person to continue growing our US presence as we experience rapid adoption of our solution in the US and across the globe. We’re thrilled to have him on board,” said Liran Hason, founder and CEO of Aporia. “Tim has worked extensively with data science teams and seen the growth of AI, and understands the opportunities and challenges that come with it. I look forward to working with Tim in expanding our global sales team that will supercharge our growth as more companies adopt Aporia’s leading ML observability solution.”
The appointment comes on the heels of Aporia announcing a $25 million Series A, led by Tiger Global, with participation from Samsung Next and the release of new capabilities, including customized visibility and monitoring, investigation, and explainability for machine learning models in production.
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