Be our first and help us build out this critical capability!

Product Data Scientists are the people who bring practices from empirical science to life to drive the Product Development cycle. They work across the team with UX researchers, feature engineers, product and business managers to create data about user behaviours, discover unmet needs, set metrics that measure performance and design & execute experiments to generate learnings and evidence their success (or lack thereof). 

This is a role where mastery in data wrangling, metric setting, statistics, experimentation and data storytelling are combined to enable product teams to become learning machines. This requires a thoughtful application of craft across the entire data journey, from data creation to behaviour change. Whether it’s creating data or generating learning, setting metrics or defining the roadmap, the product data scientist is a critical partner to every product team.

What you’ll do:

  • Driving high quality data creation from product teams: Work with product teams to help them create the data they need for performance management and learning, collaborating to design event logs and establishing the regular rhythms that enable you to do this on an ongoing basis. This will be measured by the coverage of user behaviours within our products that have high quality events that represent them.
  • Wrangle raw data into usable datasets for your own insights: Convert raw JSON event logs into usable datasets as needed to support your insights work, and collaborate with analytics engineers to embed your working knowledge of the data into production quality product data sets that the whole company can use. This will be measured by how autonomous you’re able to execute in producing insights (e.g. without the direct support of data or analytics engineers).
  • Define success metrics and supporting explanatory metric frameworks: Work closely with the product and business managers to set success metrics and define holistic explanatory metric frameworks to provide additional measures that help explain performance. This will be measured by how many products and features have success metrics defined that the product teams routinely use to understand their performance. 
  • Produce Insights & Opportunities that enable product teams to better meet their user’s needs: Use a range of methods from data visualisation to statistical inference to distill complex insights into compelling stories, with clear opportunities, which inspire product managers to make real world changes to their products and/or features. This will be measured by the number of product changes that were evidenced by your Insights or are a direct implementation of your recommended opportunities. 
  • Help with the teams carry out early testing and light rough experiments: Design and implement semi-controlled experiments, draw conclusions, produce learnings and work closely with user experience researchers to help the product teams understand the success/failures of their innovations. To begin with, experiments will have to be executed without the use of an experimentation platform! This will be measured by the number of major product features where roll out and full development is evidenced through early testing and light touch experimentation.
  • Partnering cross functionally to bring science into the product development cycle: Tune into multiple viewpoints and lingos (e.g. domain specific languages) to facilitate communication between technical and non-technical team members as needed to place data and insights at the centre of the product development lifecycle. This will be measured by the trust you build across the product team (e.g. as quantified through quarterly surveys)
  • Enabling self-service for the easy, highly repetitive stuff: Enable self-service for recurring learning needs by building data, reporting or analytics products in an interactive data visualisation environment such as Tableau or Looker. This will be measured by the number of repeating reporting & insights needs that can be satisfied without the direct support of a data professional. 

What you’ll love:

  • Being in a fast growing organisation built on the drivers of intrinsic motivation --  Freedom & Ownership, Mastery and Belonging -- and loving what you do
  • Embracing a passion for learning, fostering a growth mindset and overcoming limiting beliefs so you become 10x better than when you joined 
  • Getting to know more about what gives you energy, what you’re really good at and being supported by an experienced development champion as you pursue further mastery and drive your own development 
  • Enjoy the freedom to work where you need, when you need, taking as many holidays as you need so you can stay motivated and achieve your results in the best way possible
  • Being in a diverse organisation where we value belonging

About our culture

About Pollen

Pollen builds, curates, and delivers the best travel experiences using technology, a network of global partners, and a data-driven customer-led mindset – pairing world class entertainment with the world’s most exciting destinations.

Pollen brings you all the best experiences on We have two offerings: Pollen Presents and Pollen+. Pollen Presents curates experiences around your tastes and preferences you can’t find anywhere else. These experiences bring together music, sports, wellness and beyond to enable you and your friends to connect around one-of-a-kind itineraries in exciting destinations. Pollen+ always gets you more at music festivals and events when you book through us. Pollen+ partners with the biggest promoter brands and music festivals, including We Are FSTVL, Austin City Limits, Bonnaroo, C3, Electric Zoo, Live Nation and more. 

Pollen was founded in 2014 by brothers Callum & Liam Negus-Fancey. Pollen is backed by prominent investors including Kindred, Northzone, Sienna Capital, Backed and Draper Spirit. Pollen has raised over $100 million in VC funding.

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Diversity and inclusion is a priority for us here at Pollen and that means embracing people from all different backgrounds. 

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