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Principal Applied Scientist at Amazon
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced Machine Learning engineer and scientist (PhD and postdoctoral fellow) with a demonstrated history of problem solving in the tech industry.
QUT (Queensland University of Technology)
PhD, Computer Science
January 1, 2007 – January 1, 2011
Politecnico di Torino
Master's degree, Computer Engineering
January 1, 1999 – January 1, 2005
Amazon
Principal Applied Scientist
April 1, 2025 – Present
Amazon
Sr. Machine Learning Scientist
April 1, 2020 – April 1, 2025
Blue Apron
Machine Learning Engineering Lead
January 1, 2016 – January 1, 2018
Blue Apron
Senior Data Scientist
January 1, 2015 – January 1, 2016
Queensland University of Technology
Postdoc Fellow
January 1, 2011 – January 1, 2015
Brisbane, Australia
Queensland University of Technology
Research Assistant
January 1, 2009 – February 1, 2010
Brisbane, Australia
Queensland University of Technology
PhD Candidate
January 1, 2007 – January 1, 2011
Brisbane, Australia
Motorola Embedded Communication Computing
Software Engineer
January 1, 2005 – January 1, 2007
Telecom Italia Information Technology (TI.IT) s.r.l.
Network Security Engineer
January 1, 2004 – January 1, 2005
Campus Crowd monitoring with customized Bluetooth/Wi-Fi detectors
April 1, 2014 – Present
This experiment aims to develop innovative methods for monitoring crowd and pedestrians with the MAC addresses from their mobile devices (e.g. smartphones, laptops or tablets). The Media Access Control (MAC) address is the unique ID integrated in the network devices such as Ethernet, Wi-Fi or Bluetooth, but no link to device user’s information. By observing and matching these MAC addresses from different locations, we can study the people movements and estimate their travel time from point A to point B. The outcomes of this experiment will also provide useful reference about the reliability and stability of hardware, so that we can apply in the traffic monitoring. The scanners will be set up at several proposed locations in campuses, and capture the MAC addresses from mobile devices passing the scanner zone. Not all the MAC addresses can be captured, if the Wi-Fi or Bluetooth function has been switched off or set as undiscoverable, no MAC address will be observed by the scanner. In this experiment, the MAC address will only be used as the unique number to match at different locations, no personal information will be shown in the experiment results.
Cultural Fit Analysis
The candidate has a diverse background spanning academia (QUT) and industry (Motorola, Telecom Italia, Blue Apron, Amazon), indicating adaptability to different work environments. The progression from research to senior and principal roles in ML/Applied Science suggests a drive for growth and leadership. The personal project on crowd monitoring shows initiative and an interest in practical data applications. However, the target role of 'Data Analyst' might be a step down from their 'Principal Applied Scientist' role, which could be a cultural mismatch if the candidate is seeking a more senior, strategic, or research-intensive position. The breadth of experience suggests a good fit for organizations that value deep technical expertise and a research-oriented approach to data problems.
Soft Skills & Operational Fit
The candidate's extensive academic and industry experience suggests strong problem-solving and research skills. The project description indicates an ability to design and execute experiments, which implies good planning and analytical thinking. However, without psychometric test results or interview data, it is difficult to assess specific soft skills like teamwork, communication, or stress handling.