Senior Data Scientist, Experimentation Practice

Maximizing our potential as a learning organising is central to meeting Spotify’s Mission to unlock the potential of human creativity—by giving a million creative artists the opportunity to live off their art and billions of fans the opportunity to enjoy and be inspired by it. Business leaders and product teams depend on learnings to inform their daily decisions by understanding how people experience music through products.

What you’ll do:

Working across our embedded community of data scientists and engineers — and partnering closely with central tooling and infrastructure teams — you’ll lead Spotify’s effort to develop and make easy to adopt shared practices from science, ensuring high quality data science outputs always reflective of our combined learnings and expertise.

By engaging across disciplines you’ll develop a practice that’s easy for others to adopt and is equally easy for others to contribute to. You’ll build and maintain an evolving set of shared principles, shared tools, shared knowledge and most importantly shared behaviours for how we produce and manage data for insights across the company.

You’ll work with data scientists to cultivate a shared practice for experimentation to maximize the speed and impact of our tests. You’ll partner with our Experimentation Platform teams to make sure they’re aware of the highest priority needs of data scientists.

This is a hands on practice leadership role. As a result of your efforts, every team at Spotify will be better equipped to design and analyze experiments. Ultimately you will take on complex large scale experimentation related problems covering user and creator behaviours across our broad range of mobile and connected platforms.

Who you are

  • You’re passionate about the role of experimentation in maximizing a company’s potential as a learning organisation. Your passion for good practice is infectious. As you continuously improve your own practice, Spotify’s practice will improve alongside you.
  • You’re collaborative. You work hands on with a wide variety of teams to ensure that both the most recent needs of the community are reflected in our shared practice and that behaviours of the community are changing to adopt it. When there are competing shared practices you facilitate discussions, providing your experienced perspective where needed, in order to establish a single view, e.g. the guiding principles or when to use which practice. When infrastructure is missing or incomplete you surface the needs and work with infra teams to ensure that they are understood.
  • You’re innovative. You’re a builder. You show people the value of your ideas by bringing them to life in tangible, innovative ways. You work closely with central engineering and infrastructure teams to tackle the most complex challenges at Spotify are addressed in a sustainable way.
  • You’re playful. You’re always looking for new ways to engage across disciplines to build engagement and cultivate a shared sense of ownership of the practice.
  • You’re sincere. As community lead, you take an active interest in your own development and the development of other data scientists across the organisation.

Qualifications

  • 5+ years as a practicing data scientist.
  • A high level of ability in data science languages such as SQL and Python and/or R.
  • Some previous experience leading data scientists in developing and adopting shared practice is essential (e.g. building an internal community of practice, contributing to open source)
  • 2+ years designing and analyzing online experiments at a tech company
  • 2+ years working directly with product or business managers to carry out experiments in order to meet product or business goals (e.g. improving success metrics)
  • Ideally you have knowledge of existing best practices (e.g. multiple comparisons, stopping rules, heterogeneous treatment effects, Bayesian tests) and would be able to help drive adoption across Data Scientists at Spotify, collaborating with Experimentation Platform teams to provide infrastructure as needed.
  • Formal education in experimentation or causal inference would be considered an advantage.

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