Amina Shabbeer, PhD

Critic
DISC Type : C

Applied Science Manager at Amazon

San Francisco Bay Area, United States

Overview

Amina is an Applied Science Manager at Amazon, leading the Music ML Personalization team. With 15 years of experience in machine learning and a Ph. D. from Rensselaer Polytechnic Institute, she specializes in deep learning-based recommender models and conversational AI. People who have worked with her describe her as "smart" and "hard working".

She is a member of South Park Commons, a community for technologists, and has shown support for women in computing by attending the Grace Hopper Celebration. Her early academic research focused on bioinformatics and developing tools for the analysis of Mycobacterium tuberculosis, indicating a deep-seated interest in applying technology to complex problems.

Unique fact: She holds a patent for an optimization-based approach to load-balancing in cloud-based data warehouses, developed during her time at IBM.

Personality Overview

Precise

Information Seeker

ROI Driven

They prefer to do logical analysis and value evidence over emotions.  It is very likely that they will negotiate pricing or other important terms. Unless the value is proven by data, they are unlikely to value fancy features.

Topics They Care About

Recommender Systems
Leads a team at Amazon focused on personalization, using deep learning and multi-armed bandits to build recommender models for millions of music and podcast listeners.
Large Model Training
Actively shares technical articles and insights on parameter-efficient methods like LoRA and tools like Deepspeed for fine-tuning and training large-scale models.
Conversational AI
Previously drove the technical roadmap for spoken language understanding and dialogue management systems for the Alexa Music platform at Amazon.

Media Appearances

Amina has no verified media appearances

Work History

8-2024
Applied Science Manager at Amazon
4-2023 - 8-2024
Member at South Park Commons
1-2022 - 2-2023
Manager Applied Science (Machine Learning) at Amazon
10-2015 - 12-2021
Sr. Machine Learning Scientist at Amazon
10-2013 - 10-2015
Staff Software Engineer at IBM

Education

2008 - 2013
Doctor of Philosophy (Ph.D.) from Rensselaer Polytechnic Institute
2002 - 2006
Bachelor of Engineering (B.E.) from University of Mumbai

More Information

Social Presence :

Prographics :

Exp : 17 Location : San Francisco Bay Area, United States Job Level : Middle Designation : Applied Science Manager at Amazon
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Insights For Selling To Amina

During A Call Or A Meeting

DO's

  • Be formal and objective, they will appreciate it more
  • Leverage facts and figures wherever possible; use percentages, numbers etc.
  • Be ready for penetrating questions and critical examination of your pitch

DONT's

  • Don’t try to give too many examples of other users, they like to make their own decisions
  • Make extra effort to not seem pushy or confrontational
  • Don’t rush them till they have clearly gotten all the necessary information

When Cold Calling

When Writing An Email

While Negotiating & Closing

    The secret to closing fast with Amina is

  • Strong evidence of ROI, effective pricing, and proven data points matter the most to them.
  • Will you ever get a clear answer from Amina

  • It is not very hard for them to say no if they are not convinced about the decision.

Insights For Deal Planning

    How fast (or slow) will Amina move?

  • Their decision-making is neither very fast nor very slow, they are somewhere in between.
  • Can Amina take some risk or not?

  • They can take risks if their analysis shows that it would be worth it.

You And Amina

Personality Compatibility


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