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Software Engineer: Machine Learning at Facebook, specializing in Natural Language Processing
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Brandeis University
Master of Arts (M.A.), Computational Linguistics
January 1, 2013 – January 1, 2015
Jagiellonian University
Bachelor of Arts (B.A.), Slavic Languages, Literatures, and Linguistics
January 1, 2007 – January 1, 2011
Software Engineer, Machine Learning
June 1, 2021 – Present
Greater Boston
Luminoso Technologies, Inc.
Senior NLP Research Scientist
January 1, 2017 – May 1, 2021
Cambridge, MA
CallMiner
Research Engineer
June 1, 2015 – December 1, 2016
Greater Boston Area
CallMiner
Research Intern
January 1, 2015 – May 1, 2015
Greater Boston Area
Genesys
Software Development Intern
May 1, 2014 – December 1, 2014
Greater Boston Area
Cultural Fit Analysis
The candidate has a strong background in research and development within NLP and ML, working for both startups (Luminoso, CallMiner) and large tech companies (Facebook). This diversity suggests adaptability. However, the target role is 'Data Analyst,' which might be a shift from their primary focus on 'Software Engineer, Machine Learning' and 'NLP Research Scientist.' While their skills are highly relevant, the exact alignment with a pure 'Data Analyst' role needs further exploration to ensure cultural fit with typical data analysis responsibilities (e.g., dashboarding, reporting, business intelligence) versus pure research/ML engineering.
Soft Skills & Operational Fit
The candidate's experience at Luminoso Technologies mentions communicating research ideas and outcomes to non-technical audiences, indicating strong communication skills. The role also involved developing and maintaining open-source tools, suggesting collaboration and community engagement. However, specific details on other soft skills like leadership, problem-solving under pressure, or team collaboration are not explicitly provided in the given data.