Who We Are

UnitedMasters is building a marketplace that connects artists, brands and fans - empowering artists to earn and grow. UnitedMasters has taken the bold step of building a music distribution service that, radically, puts artists first - disrupting the legacy music business by letting up-and-coming artists distribute their music directly to fans through streaming services while maintaining ownership of their master recording rights and up to 100% of royalties. Through the combination of UnitedMasters’ music distribution platform and its deep ties to brands, UnitedMasters enables independent artists and change-makers to grow and earn unlike any other platform.

The UnitedMasters team is made up of musicians, marketers, engineers, and storytellers with backgrounds from YouTube, SoundCloud, Pandora, Facebook, Uber, Dropbox, Complex, VICE, and more. We work hand in hand with the award-winning creative teams that forge those innovative partnerships at Translation (our in-house creative advertising agency).

Position is based at the employer’s office in San Francisco, CA with a hybrid work arrangement. Employee is required to work on-site at the San Francisco office four (4) days per week and may work remotely from home on the remaining day. Employee must reside within a normal commuting distance of the San Francisco office

What You’ll Do

• Work with Music, Product and Engineering teams to develop and apply statistical methods to transform large amounts of structured and unstructured data into predictive models to drive product management.
• Apply machine learning techniques (including logistic regression, decision trees, random forests, and k-means) and experimentation frameworks to design and develop explanatory and predictive models from consumer behavior data to identify niche markets, target specific demographics, and optimize marketing efforts.
• Prototype algorithmic implementations and testing efficacy using appropriate experimental design and hypothesis validation.
• Build and optimize data pipelines and feedback loops into models to assist with business decision making.
• Assist with the development of in-house tools to analyze and process large data sets.
• Research new data sources, build proof of concepts, and maintain processes to ensure quality and integrity. Develop robust measurement strategies across experimentation, modeling, and analytics.
• Gather, extract, and compile data sources across relevant tools like SQL and Python.

Minimum Qualifications

• Masters degree in Statistics, Econometrics, or another math- or statistics-related field plus one (1) year of experience in the job offered or as a data scientist, data analyst, or other closely related position. Related experience must include:
  • Econometrics
  • Experience with machine learning techniques including logistics regression, decision trees, random forests, and k-means
  • Experience developing in-house tools to analyze and process large data sets
  • Experience with Python, SQL and experimentation frameworks.

Compensation

Salary Hiring Range: $215,000- $220,000 

Our salary ranges are based on paying competitively for our size and industry, and are one part of many compensation, benefits, and other reward opportunities we provide (e.g. in some cases bonus, stock options, 401(k) match, etc.).

Individual pay rate decisions are based on a number of factors, including qualifications for the role, experience level, skillset, and balancing internal equity relative to peers at the company. We expect the majority of the candidates who are offered roles at our company to fall healthily throughout the range based on these factors.

The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be less experienced (or more senior) than this job description as posted. If that ends up being the case, the updated salary range will be communicated with you as a candidate.

The salary range above is for NY/CA. As a company, we have a location-based strategy, which means the salary range could be lower or higher than this if the role is hired in another location.