I am a Machine Learning specialist with a background in and a passion for music related applications and data.
I have a PhD in Music Informatics and offer Data Science services to startups and larger entities.
If R&D and development are still my main focus, I have gradually extended my services to support projects end-to-end:
Advice, Strategy, Technology Watch
R&D, Experimentation, Prototyping
Development of scalable, integrated and maintained production code
Project, Product and Team management
Talent Sourcing of specialised profiles in Music Informatics
Teaching, Training, Mentoring
Reviewing solutions or projects
Contact Me
IMPRESSUM
Amélie Anglade
Belforter Str. 20, 10405 Berlin, Germany
amelie.anglade+contact@gmail.com
Registered in Berlin, Germany as a freelancer (Freiberuflich)
VAT-ID-No.: DE 296 649 676
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Research, prototyping and implementation of Search engines, Recommendation Systems and Machine Learning algorithms for Music Discovery. Also worked on signal processing and audio fingerprinting as well as Women in Tech outreach programmes.
In charge of building recommendation/personalisation features as well as the entire web backend development. Leading the developer team, onboarding, training and supervising programmers across the entire stack.
Performed product-focused Data Science tasks for the mobile application Bertha, and surrounding services. Took care of the entire Data Science stack: designing, prototyping, testing and scaling Data Science algorithms, including Big Data Engineerings tasks. Collaborated with backend engineers, dev ops, designers and product owners to fully integrate Data Science into their code, work and processes while applying an agile methodology to the Data Science processes as well, including quick iterations and bi-weekly demos of data science results. Trained colleagues, supervised working students, recruited and onboarded data scientists.
PhD candidate, affiliated with the Centre for Digital Music and the OMRAS2 project (large UK project funded by the Engineering and Physic Sciences Research Council). Conducted research and development on knowledge representation of musical harmony and modelling of genre, composer and musical style using Machine Learning techniques and Logic Programming.
Research and implementation of clustering techniques to create and maintain user communities that share specific music tastes in order to build P2P radio channels.
Teach Python for Data Science, including an overview of the language and its ecosystem, as well as the libraries essential to a Data Scientist (numpy, Pandas, scikit-learn, etc.).
Recently created a new course on Freelancing in Data Science, for current batches and alumni.
Mentored students of earlier batches, providing advice on their portfolio projects as well as general career advice in the field of Data Science.
