Richa Namballa
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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
Machine Learning
Coursera
June 24, 2026 – Present
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
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.