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Applied Science Manager at Amazon
• Motivated tech leader with 9 years of experience in applying statistics, machine learning and deep learning on industrial solutions, such as image recognition for offline retail execution, fraud detection for online payment, user behavior profiling for cloud customers and cyber-physical systems. • Ph.D. in Electrical Engineering and MS in Applied Math with solid advanced mathematics and engineering background. • Effective communication and presentation skills. Ability to convey ideas to groups with diverse backgrounds and levels. • Enthusiastic to learn new concepts and techniques. A frequent speaker at conferences on machine learning applications. • US permanent resident.
University of Notre Dame
Master of Science, Applied Mathematics
January 1, 2011 – January 1, 2013
University of Notre Dame
Master of Science, Electrical Engineering
January 1, 2008 – January 1, 2010
University of Notre Dame
Ph.D., Electrical Engineering
January 1, 2008 – January 1, 2014
Harbin Institute of Technology
B.E., Automatic Control
January 1, 2004 – January 1, 2008
Amazon
Applied Science Manager
October 1, 2019 – Present
Greater Seattle Area
University of Washington
Course Instructor and Developer
January 1, 2019 – May 1, 2019
Clobotics
Lead Machine Learning Scientist
July 1, 2017 – July 1, 2019
Greater Seattle Area
Microsoft
Senior Data Scientist
May 1, 2015 – June 1, 2017
Greater Seattle Area
Amazon
Research Scientist
May 1, 2014 – May 1, 2015
Seattle, Washington
Air Products
Data Modeling and Optimization Summer Intern
May 1, 2013 – August 1, 2013
Allentown, Pennsylvania Area
University of Notre Dame
PhD Candidate
May 1, 2012 – April 1, 2014
Dow AgroSciences
Business Data Analysis Summer Intern
May 1, 2012 – August 1, 2012
Indianapolis, Indiana Area
University of Notre Dame
Teaching Assistant
August 1, 2010 – December 1, 2012
University of Notre Dame
Research Assistant
August 1, 2008 – April 1, 2014
Microsoft AI School
January 1, 2017 – April 1, 2017
Kaggle Data Science Bowl 2017 - Lung Cancer Detection Using 3D CNN and CNTK
Cultural Fit Analysis
The candidate's background includes diverse roles across large corporations (Amazon, Microsoft), a startup (Clobotics), and academia (University of Washington, Notre Dame). This breadth of experience suggests adaptability to different organizational cultures and work environments. Their involvement in projects like Kaggle Data Science Bowl and various research initiatives indicates a proactive and continuous learning mindset, which aligns well with innovative and data-driven cultures. The target role of 'Data Analyst' might be a slight under-match for their extensive experience in 'Applied Science Manager' and 'Lead Machine Learning Scientist' roles, which typically involve more advanced modeling and strategic responsibilities.
Soft Skills & Operational Fit
The candidate's experience as a Lead Machine Learning Scientist and Applied Science Manager suggests strong leadership, mentorship, and cross-functional collaboration skills. Their role as a Course Instructor also indicates an ability to communicate complex topics effectively. The descriptions imply a results-driven and problem-solving mindset, which is crucial for operational fit in a data-intensive role.