Data Scientist, Marketing Bidding Models
Posted on Indeed on Feb 05, 2021
We believe a large part of building an effective insurance company can be solved with a principled quantitative framework. We are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.

Working alongside our Growth Marketing team, we're looking for a Data Scientist to optimize the performance of our marketing spend. In this role, you'll work closely with marketing channel managers to understand the levers within our marketing channels, designing bidding strategies, constructing look-a-like audiences, and implementing other tactics to improve our marketing efficiency.

Positions are available at different levels of seniority, based on past experience.


  • Learn and understand the levers available within each of our marketing channels
  • Identify opportunities to use quantitative approaches to improve our marketing ROI
  • Build models in support of opportunities for improved efficiency
  • Design experiments to prove out the efficacy of proposed solutions
  • Work with channel managers to implement and monitor results of experiments
  • Communicate results of complex analysis clearly and concisely to non-technical audiences
  • Take end-to-end ownership of problem domains and continuously improve upon solutions


  • 5+ years of applying advanced quantitative techniques to problems in industry
  • Demonstrated experience building, validating, and applying statistical machine learning methods to real world problems
  • Strong demonstrable knowledge of topics such as statistical inference, numerical linear algebra, machine learning, and numerical optimization
  • Strong programming skills with experience using modern packages in R and Python, as well as proficiency using SQL
  • Experience applying quantitative and statistical approaches to optimizing marketing or sales, preferred
  • Experience in quantitative trading or other results-focused quantitative roles, preferred

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