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PhD Candidate in Music Technology at NYU
• Current PhD candidate in the Music and Audio Research Laboratory (MARL) at New York University working under the advisement of Prof. Magdalena Fuentes. • Previous Master's student in the Music Technology program at New York University focusing on music information retrieval applications, audio engineering, and music cognition. • Former Silicon Valley Senior Data Scientist with 5+ years of industry experience in taking data science and machine learning models from proof-of-concept to production. • Previous projects involve computer vision, natural language processing (NLP), and traditional statistical modeling in procurement applications. • Mission is to ensure that data science and machine learning are only used where appropriate and that models are practical, scalable, tested, and value-driven rather than exhibitionist. • History of leadership and clear, concise communication with the ability to convey complex technical concepts in layperson's terms. • Areas of interest include: music technology, music information retrieval and informatics, sports analytics, social applications of statistics.
New York University
Doctor of Philosophy (Ph.D.), Music Technology
September 1, 2023 – Present
New York University
Master of Music (M.M.), Music Technology
September 1, 2021 – May 1, 2023
University of California, Berkeley, Haas School of Business
Bachelor of Science (B.S.), Business Administration
August 1, 2014 – May 1, 2016
University of California, Berkeley
Bachelor of Arts (B.A.), Statistics
August 1, 2012 – May 1, 2016
Deezer
Research Intern
May 1, 2026 – Present
Paris · On-site
New York University
Adjunct Instructor
September 1, 2024 – Present
New York, United States · On-site
New York University
Graduate Teaching Assistant
January 1, 2024 – May 1, 2024
New York, United States · On-site
Emursive Productions
Audio Engineer
August 1, 2022 – January 1, 2025
New York, United States
New York University
Assistant Studio Engineer
May 1, 2022 – August 1, 2022
New York, United States
New York University
Research Assistant
October 1, 2021 – May 1, 2023
New York, United States
SAP Ariba
Senior Data Scientist
April 1, 2021 – August 1, 2021
Palo Alto, California, United States
SAP Ariba
Data Scientist
July 1, 2019 – March 1, 2021
Palo Alto, California, United States
SAP
Data Scientist
July 1, 2017 – June 1, 2019
Palo Alto, California, United States
SAP
Developer Associate
July 1, 2016 – July 1, 2017
Palo Alto, California, United States
Fitbit
Human Resources Assistant
June 1, 2015 – September 1, 2015
San Francisco Bay Area
UC Berkeley Intercollegiate Athletics
Development Intern
September 1, 2014 – June 1, 2016
Berkeley, CA
Social and Behavioral Research - Basic/Refresher
CITI Program
June 24, 2026 – Present
Social and Behavioral Research
CITI Program
June 24, 2026 – Present
Complete Guide to TensorFlow for Deep Learning with Python
Udemy
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background shows a blend of academic research, teaching, and industry experience in data science. The transition from data science to a Ph.D. in Music Technology with a focus on AI Music Detection indicates a strong passion for specialized ML applications and continuous learning. This aligns with a culture that values innovation, research, and deep technical expertise. The diverse roles, including audio engineering, suggest adaptability and a broad skill set. However, the lack of explicit project details makes it difficult to assess collaboration styles or specific contributions to team-based engineering efforts beyond teaching assistant roles.
Soft Skills & Operational Fit
The candidate's experience as an Adjunct Instructor and Graduate Teaching Assistant suggests strong communication and pedagogical skills. Research roles indicate an ability for independent problem-solving and structured inquiry. The diverse academic background (Music Technology, Statistics, Business) points to an interdisciplinary approach, which can be beneficial in complex ML projects. However, the operational fit for a pure ML Engineer role might require more explicit demonstration of MLOps, deployment, and large-scale system integration experience.