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Senior Data Scientist (Ph.D. in a scientific discipline (e.g., Applied Statistics/Mathematics, Computer Science, Engineering, or Physics) 122 views

DATA SCIENTIST
We’re looking for a Data Scientist who would push the boundaries of applying cutting-edge scientific methods and drive the development of innovative data-driven solutions within the insurance industry.

But first, get to know us:
The firm is a group of tech-forward, seasoned insurance experts ready to make a real change in our industry. We believe there’s a better way to do commercial insurance – a way that starts with a clean slate, free from inefficiency and outdated technology. We’re building a state-of-the-art, tech-enabled platform that delivers faster underwriting and claims services, giving brokers and companies more time to focus on what’s important: building their businesses. If you’re ready to join the revolution, and work obsessively with us to make insurance better, faster, and more efficient, then come join us.

As a Data Scientist, here are some of the things you will do:

  • Provide innovative solutions for business problems developed using rigorous scientific approaches.
  • Use data (including third party data) to fuel automation and decisions within our company.
  • Develop and employ AI algorithms that can find (predictive) patterns in large multi-modal data.
  • Work in highly cross-functional and collaborative teams together with developers, designers, analysts, underwriters and claims adjusters.
  • Take ownership of development and implementation of ideas and solutions.

Required Skills/Experience:

  • Ph.D. in a scientific discipline (e.g., Applied Statistics/Mathematics, Computer Science, Engineering, or Physics).
  • 3+ years industry experience post PHD
  • Scientific expertise and applied experience in Machine Learning.
  • Foundational knowledge of Natural Language Processing (NLP).
  • Knowledge of advanced statistical theories, methodologies, and inference tools (e.g., Bayesian inference, hierarchical models, deep learning, Gaussian processes, and causal inference).
  • Advanced programming skills in Python and/or R (and their related data processing, Machine Learning, and visualization libraries).
  • Practical experience in preparing data for Machine Learning.
  • The ability to work independently, anticipate problems, and forge a path into uncharted territory.
  • Excellent (written and oral) communication skills.

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