Miro Enev, PhD

Editor
DISC Type : CS

Manager, Solution Architecture & Data Science at NVIDIA

Seattle, Washington, United States

Overview

Miro Enev is a Solution Architect Manager at NVIDIA, leading a team focused on Cloud AI and Accelerated Workloads. With a PhD from the University of Washington and a background in Cognitive Science, he specializes in applying deep learning to modern business challenges. Colleagues describe him as creative and technically knowledgeable.

He has a stated interest in advancing data science while respecting human values in technology. His previous role as a Principal Data Scientist at Phyn focused on IoT-based water conservation, showcasing an interest in technology for sustainability. He is also concerned with data privacy in automated systems.

Unique fact: Miro co-authored a publication on "Automobile Driver Fingerprinting," a method for identifying individuals based on their unique driving patterns using in-car sensor data.

Personality Overview

Self-Disciplined

Objective Thinker

Sometimes Friendly

The only way to convince them is by showing them examples and ample proof.  They are always well-planned and adopt a systematic approach. They are heavily focused on quality and prefer doing things the right way, even if it takes time.

Topics They Care About

Cloud AI Workloads
His current management role at NVIDIA is focused on Cloud Solution Architects for partners like Microsoft Azure and AWS, applying AI to cloud environments.
Applied Deep Learning
A core theme of his career is applying the latest AI research, specifically deep learning, to solve practical business problems for customers.
Human-Centric AI
He has a stated interest in ensuring that the advancement of machine intelligence respects human values in future technology ecosystems.

Media Appearances

Miro has no verified media appearances

Work History

1-2023
Manager, Solution Architecture & Data Science at NVIDIA
5-2020 - 1-2023
Principal Engineer & Data Scientist at NVIDIA
10-2016 - 5-2020
Senior Solutions Architect & Data Scientist at NVIDIA
8-2016 - 10-2016
Principal Data Scientist at Phyn
4-2014 - 8-2016
Senior Machine Learning Engineer at Belkin

Education

2008 - 2014
Doctor of Philosophy (Ph.D.) from University of Washington
2001 - 2005
Bachelor of Arts (BA) from University of California, Berkeley

More Information

Social Presence :

Prographics :

Exp : 12 Location : Seattle, Washington, United States Job Level : Middle Designation : Manager, Solution Architecture & Data Science at NVIDIA
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Insights For Selling To Miro

During A Call Or A Meeting

DO's

  • When following up with them, expect slowness; use questions to engage them, preferably over email.
  • Use phrases like 'results based on data', 'measurable proof', 'X% growth' etc.
  • Share whitepapers or case studies that showcase measurable results instead of just telling customer stories

DONT's

  • Avoid winging it with them particularly, answer a question only if you know the answer well
  • Do not sound overly eager, modulate your energy levels especially if you are generally enthusiastic
  • Avoid phrases like ‘trust me’, ‘you will just love it’ etc.

When Cold Calling

When Writing An Email

While Negotiating & Closing

    The secret to closing fast with Miro is

  • Proof of usage by others in the industry, case studies showing ROI are likely to work the best with them.
  • Will you ever get a clear answer from Miro

  • They are unlikely to say no, it's better to stop yourself once you have exhausted all the options.

Insights For Deal Planning

    How fast (or slow) will Miro move?

  • They are some of the slowest movers and take their time reaching decisions.
  • Can Miro take some risk or not?

  • They have very low acceptance of risk even if they do not say it directly.

You And Miro

Personality Compatibility


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