Data Scientist

About the Role

As our Founding Data Scientist on the Applied Product team, you will establish the data-driven product development culture for B2B products at Polser.

You should expect to define our north-star metrics, design our first A/B tests, and establish source-of-truth dashboards that the entire company can use to answer their own product questions. Most importantly, you should expect to be a core member of the product development team building our first party offerings.

This role is based remote but EU prefered.


In this role, you will:

  • Embed with the product development team as a trusted partner, uncovering new ways to improve the product and drive growth
  • Define and interpret A/B tests that help answer critical questions about the impact of model and UX changes to our product
  • Establish a data-driven product development culture by driving the definition, tracking, and operationalizing of feature-, product-, and company-level metrics
  • Develop and socialize dashboards, reports, and other ways of enabling the team and company to answer product data questions in a self-serve way


You might thrive in this role if you have:

  • 5+ years experience in a quantitative role navigating highly ambiguous environments, ideally as a founding data scientist or product analyst at a hyper-growth product company or research org
  • Proposed, designed, and run rigorous experiments with clear insights and product recommendations utilizing SQL and Python
  • Defined, implemented, and operationalized new feature and product-level metrics from scratch
  • Excellent communication skills with demonstrated ability to communicate with product managers, engineers, and executives alike
  • Strategic insights beyond the paradigm of statistical significance testing


You could be an especially great fit if you have:

  • Demonstrated prior experience in NLP, large language models, or generative AI
  • Strong programming background, with ability to run simulations and prototype variants
  • Experience validating quantitative insights with qualitative methods (e.g. surveys, UXR)
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