CLEAR’s mission is to strengthen security and create frictionless experiences. We believe you are you and by using your biometrics – your eyes, face, and fingerprints – 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 doctor’s office, access your office building, and more – without ever pulling out your wallet. CLEAR is currently available in 50+ airports, stadiums and venues nationwide. Now with Health Pass, CLEAR securely connects a person’s digital identity to multiple layers of COVID-related insights to help reduce public health risk and restore peace of mind.
We’re defining and leading an entirely new industry, obsessing over our customers, and investing in great people to lead the way. Recently named on CNBC’s Disruptor 50 List for the second year in a row and winner of the SXSW Interactive Innovation Award, CLEAR is providing innovative technology options for businesses and our 5+ million members to help create a safer environment no matter where you go.
We’re seeking an innovative and 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, and in some cases IP creation. You’re a deep thinker who is intellectually curious and enjoys solving critical problems. You are a self starter who can own a solution from end to end.
You are technically proficient and have the ability to access and wrangle large amounts of structured and unstructured data, a great business sense, the desire to influence strategic decisions with data-driven analysis. You think deep, you happily prove your assumptions and you work fast. Lastly, you have strong written and 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 and domain independent data.
- Work collaboratively with the data science and product management teams to evolve current and 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 and existing data is highly preferred.
- You have a strong desire to work in a highly collaborative, team oriented, intellectually curious environment.
- Comfortable scoping and structuring your work in the face of a variety of different problems types such as deterministic problems, amorphous, ambiguous, and 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.) and 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 and 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 and 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 and SQL in a real-world context.