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CLEAR // biometric identity platform
 
Engineering, Full Time    New York City, United States    Posted: Friday, June 26, 2020
 
   
 
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JOB DETAILS
 

CLEARs mission is to strengthen security & create frictionless experiences for consumers. We believe you are you & by using your biometrics - your fingerprints, eyes, & face - we keep you moving. Imagine a world where you can do virtually everything you need to breeze through the airport, buy a beer at the game, check-in at the doctors office, access your office building, & more without ever pulling out your wallet or phone. Now in 60+ airports & other venues nationwide, you are your ID, credit card, ticket, reservation & more with CLEAR.

Were defining & leading an entirely new industry, obsessing over our customers, & investing in great people to lead the way. Recently named on CNBCs Disruptor 50 List & winner of the SXSW Interactive Innovation Award, we're working tirelessly to create frictionless customer experiences for our 4+ million members across the country.

Were seeking an innovative & results-oriented Data Scientist to identify actionable insights within our New Vertical business unit. As a critical member of the team, you will have a prominent voice in the future of the product from conception to launch, & in some cases IP creation. Youre a deep thinker who is intellectually curious & enjoys solving critical problems. You are a self starter who can own a solution from end to end.

You are technically proficient & have the ability to access & wrangle large amounts of structured & unstructured data, a great business sense, the desire to influence strategic decisions with data-driven analysis. You think deep, you happily prove your assumptions & you work fast. Lastly, you have strong written & verbal communication skills to translate the complex to the organization as a whole.

This role requires you to have unrestricted work authorization to work in the United States.


What You Will Do:

  • In house subject matter expert for a machine learning related product line, including the creation of algorithms.
  • Understand ground truth, create training models, devise new statistical models, using machine learning techniques within the context of domain specific & domain independent data.
  • Work collaboratively with the data science & product management teams to evolve current & build new quantitative product features.

Who You Are:

  • Experience taking quantitative features to market.
  • Experience modeling risk related problems, particularly those with class imbalances is highly preferred.
  • Experience conceiving of new metrics based on synthesis of new & existing data is highly preferred.
  • You have a strong desire to work in a highly collaborative, team oriented, intellectually curious environment.
  • Comfortable scoping & structuring your work in the face of a variety of different problems types such as deterministic problems, amorphous, ambiguous, & otherwise heuristic ones as well.
  • Have at least an M.S. (preferred) or Bachelors (required) in Computer Science, Operations Research, Computational Economics, Statistics, Applied Mathematics, Data Science, or related major.
  • Demonstrable hands-on experience in Machine learning (Bayesian Analysis, Decision Trees, Random Forests, Boosted Trees, Support Vector Machines, Neural Networks, etc.) & Advanced mathematics to create product features.
  • 5+ years experience leveraging the Python Data Science stack (scikit-learn, Numpy, Pandas, etc.) to drive prototyping of large data sets. Experience with auto model building tools such as DataRobot, AutoML, et al. is highly desired.
  • Skilled in cleaning, transforming & otherwise statistically describing data for the purpose of feature engineering. Experience with Feature Tools or similar is highly preferred.
  • Proficient in leveraging a variety of visualization packages & applications such as Tableau, Looker, matplotlib, Python dash, plotly, et al. to expose meaningful insights in data.
  • Experience working with data warehouses and/or relational databases & SQL in a real-world context.
 
 
 
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