Work closely with cross-functional teams of data scientists, product managers and engineers who are passionate about our consumer experience across platforms and partners.
Surface valuable user behavior insights, from aggregate trends to individual device interactions.
Communicate data-driven insights, working with the product team to build strong narratives and direct org-wide investments.
Use ML to supervise existing services and build new products personalized by the user ubiquity experience.
Who you are
You have relevant experience, with a degree in statistics, mathematics, computer science, engineering, economics or another quantitative subject area.
You have strong interpersonal skills and are comfortable working with a cross-functional stakeholder group.
You know how to translate business concerns into data problems and come up with relevant answers and impactful insights.
Experience with Google BigQuery or proficiency in SQL.
Proficiency with Python or R.
Experience in data pipeline / workflow development with large datasets (Scala experience preferential).
Experience with applied machine learning for clustering, classification, and regression.
Experience with statistical modelling (such as time series forecasting and A/B testing).
Experience with data visualization and dashboarding (Tableau or Python/R plotting libraries).
Experience crafting analytical data layers and in conducting ETL with large and complex data sets.
Cloud platform familiarity and competency e.g GCP.
Where you'll be
We are a distributed workforce enabling our band members to find a work mode that is best for them!
Where in the world? For this role, it can be within the EMEA region in which we have a work location and is within working hours.
Working hours? We operate within the Central European and GMT time zones for collaboration and ask that all be located in that time zone.
Prefer an office to work from home instead? Not a problem! We have plenty of options for your working preferences. Find more information about our Work From Anywhere options here.