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Machine Learning Music Specialist

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At Epidemic Sound we are reinventing the music industry. Our carefully curated music catalogue, with over 30,000 tracks, is tailored for storytellers no matter what their story is. Countless customers around the world, from broadcasters, productions companies and YouTubers rely on our tracks to help them tell their stories. Epidemic Sound’s music is heard in hundreds of thousands of videos on online video platforms such as YouTube, Facebook, Twitch and Instagram. Our HQ is located in Stockholm with offices in NYC, LA, Hamburg, Amsterdam and Madrid. We are growing fast, we have lots of fun and we are transforming the music industry.

The growth of our business requires us to be excellent at building and maintaining relationships with our customers to inspire action and loyalty. To achieve this, we need to make our user experience next level, through intuitive machine learning and customer analytics.

We are now looking for an experienced Machine Learning Music Specialist (you’re likely currently a Data Scientist, or perhaps an advanced Insight Analyst) who’s had the opportunity to use Machine Learning in a commercial environment.


Job Description

The position as a Machine Learning Music Specialist will report directly in to the CTO, in a fresh new team which functions as a de-centralised squad, delivering advanced analysis and machine learning to various departments throughout the company. You’ll work alongside Data Engineers and Developers to deploy microservices solving many different business needs. The use cases range from Customer Lifetime Value & Churn prediction – to building fantastic recommender engines to further personalize Epidemic Sound’s offering.

You will be working closely with the backend data team in developing robust, scalable algorithms. You will improve the personalization of the product by:

  • Analysing behaviours of visitors, identifying patterns and outliers which can indicate their likelihood to Churn
  • Developing classification systems through feature extraction on music to identify type & ‘feel’ of any given content
  • Creating recommender engines so that the music our users see first, is relevant to them based on their behaviours
  • Contributing to the automation of previously manual tasks, by leveraging the classification systems you’ve contributed to building
  • Consulting on appropriate implementation of algorithms in practice – and actively identifying new use cases that can help improve Epidemic Sound!

What are we looking for?

We’re looking for a team member with a “no task is too small” mindset – we are at the beginning of our Machine Learning journey – so we need someone who thinks building something from scratch sounds exciting. 

It would be music to our ears if you are/have:

  • Deep understanding of machine learning (neural networks, deep learning, classification, regression)
  • Experience with machine learning in production and experience with MIR, signal processing and/or music theory
  • Experience with: tensorflow, keras, pytorch, sciki-learn, scipy, numpy, pandas or similar 
  • Experience with ML projects in customer value or music information retrieval (MIR)
  • Some understanding of ML and knowledge of state of the art in ML for music
  • Fluency in python programming and a passion for production ready code
  • Experience from Google Cloud and/or AWS
  • MSc in a Quantitative or Computer Science based subject (Machine Learning, Statistics, Applied Mathematics)
  • Familiar with musical terms like timbre, harmony, tempo, key signature, etc.
  • Familiar with MIR/audio analysis terms like acoustic fingerprinting, phase vocoder, short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCCs)

Curious about our music? Find our music on Spotify here → https://open.spotify.com/user/...


Do you want to be a part of our fantastic team? Please apply by clicking the link below!

Apply for this job

Or, know someone who would be a perfect fit? Let them know!


HQ (Stockholm)

Åsögatan 121
SE11624 Stockholm Directions info@epidemicsound.com

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