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Senior Machine Learning Engineer @ Adobe Sensei
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Columbia University
Master of Science - MS, Data Science
January 1, 2017 – January 1, 2018
Vellore Institute of Technology
Bachelor of Technology - BTech, Computer Science
January 1, 2013 – January 1, 2017
Adobe
Senior Machine Learning Engineer
June 1, 2022 – Present
San Francisco, California, United States
Microsoft
Data and Applied Scientist II
March 1, 2021 – June 1, 2022
Cambridge, Massachusetts, United States
Microsoft
Data and Applied Scientist
March 1, 2019 – June 1, 2022
Cambridge, Massachusetts, United States
Chegg Inc.
Data Scientist (NLP)
June 1, 2018 – August 1, 2018
Santa Clara, California, United States
Columbia University in the City of New York
Course Assistant in NLP
January 1, 2018 – April 1, 2018
New York, New York, United States
Universität Mannheim
NLP Research
December 1, 2016 – May 1, 2017
Mannheim, Baden-Württemberg, Germany
University of Thessaly
Summer School
July 1, 2016 – July 1, 2016
Greece
Cultural Fit Analysis
The candidate has a strong background in Machine Learning Engineering and Data Science, which aligns with analytical and data-driven cultures. However, the target role is 'Data Analyst', which is a step down from their current and previous senior ML/Data Scientist roles. This might indicate a potential mismatch in career trajectory or expectations, or a desire to pivot. The breadth of projects suggests adaptability, but the depth of experience is more aligned with advanced ML roles than a typical Data Analyst role. The lack of explicit project details beyond high-level descriptions makes it difficult to fully assess cultural fit for a specific team.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest a strong problem-solving orientation and the ability to work on complex, impactful projects. The roles at Microsoft indicate a capacity for independent research and development, as well as collaboration on larger initiatives. However, without direct assessment data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively evaluated.