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AI Lifecycle Manager, Clinical Imaging SW Suite, Philips
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
The Johns Hopkins University
MSE, Biomedical/Medical Engineering
January 1, 2010 – January 1, 2012
Technion - Israel Institute of Technology
Bs.C, Biomedical Engineering
January 1, 2005 – January 1, 2009
Philips
AI Lifecycle Manager
May 1, 2025 – Present
Hybrid
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August 1, 2022 – June 1, 2023
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Machine Learning Team Leader
May 1, 2017 – August 1, 2022
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Algorithms Technical Leader / Project Manager
January 1, 2015 – December 1, 2015
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Algorithms Technical Leader
September 1, 2014 – April 1, 2017
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Sr. Algorithm Engineer
June 1, 2013 – August 1, 2014
iSonea Limited
Head of Algorithm Team
August 1, 2012 – May 1, 2013
iSonea Limited
Algorithms Consultant
August 1, 2010 – April 1, 2012
iSonea Limited
Algorithms Engineer
August 1, 2009 – August 1, 2010
Cultural Fit Analysis
The candidate has a long tenure within the medical device industry (Medtronic, Philips, iSonea Limited), indicating a strong fit for structured, regulated environments. The progression into AI leadership roles suggests an adaptability to evolving technical landscapes. However, the candidate's experience is heavily concentrated in AI/ML leadership and algorithm development, which is distinct from a pure Data Analyst role. While there's overlap in data understanding, the primary focus appears to be on model development and management rather than exploratory data analysis, reporting, or business intelligence, which are core to many Data Analyst positions. This might indicate a potential mismatch with the specific day-to-day responsibilities of a Data Analyst, depending on the exact nature of the target role.
Soft Skills & Operational Fit
The candidate's career progression from engineer to various leadership roles (Team Leader, Head of AI, AI Lifecycle Manager) suggests strong leadership, project management, and strategic thinking skills. The focus on AI lifecycle management implies an understanding of operationalizing AI solutions. However, without specific project details or direct assessment, the clarity of communication and collaboration style cannot be fully determined.