Data Scientist (VC Backed Top 20 Music Company Globally)

Beatdapp is a venture-backed startup delivering the most advanced music streaming fraud and audit technology in the world.

Our industry-leading software helps music labels and artists fight the over $2 billion royalties stolen from them each year.

Plus, who doesn't love working with the world’s best streaming services, music labels, and artists all day!

We’re growing 3x in size this year and plan to continue hiring the best candidates across Canada. After resume submission for this role, a select few will be asked to fill out a short 2 minute questionnaire.

Data Scientist

As a Data Scientist, you’ll work closely with our leadership team, data engineers, and product team to develop supervised and unsupervised models that help identify and fight fraud across music streaming services.

You will directly influence our product decisions and help to fight a multi-billion dollar problem plaguing the music industry.

Responsibilities

• Develop and train supervised and unsupervised models using billions of data points to identify fraud and bad actors
• Use strong business acumen, as well as an ability to communicate findings, and mine vast amounts of data for useful insights
• Use a combined knowledge of computer science and applications, modelling, statistics, analytics and math to solve complex problems
• Extract and enrich data from multiple sources globally
• Analyze data from multiple angles, looking for trends that highlight problems or opportunities
• Communicate important information and insights to company stakeholders
• Lead a growing data team to make strategic recommendations

Successful Candidates will have

• 3+ years experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.

and their real-world advantages / drawbacks

Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.

and experience with applications

• Experience working with and creating data architectures
• Strong problem solving skills with an emphasis on product development
• A drive to learn and master new technologies and techniques

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