February 2, 2021

Data Scientist



ABOUT US

Waycare’s proprietary deep learning technology provides cities with predictive analytics and real-time decision making tools, enabling proactive traffic and incident mitigation and dynamic real-time traffic flow optimization. As connectivity and innovation in the automotive industry continue to evolve, Waycare will play a vital role in shaping daily movements within our cities.

Using Artificial Intelligence (AI) Waycare predicts locations where dangerous incidents are likely to occur, empowering municipalities to make better-informed, proactive decisions. We are looking for a capable, independent thinker to join our team and make a difference in the world of transportation safety.

RESPONSIBILITIES
  • Work with diverse data sources to gain insight into the traffic patterns of cities
  • Discover regularities and detect irregularities in near-real-time
  • Provide predictions about high-risk areas and events that may adversely impact regular road operations and suggest ways to mitigate them
  • Study and develop models of traffic flow under various conditions and constraints
  • Work with other data scientists as well as other teams (Product, Back-End and Data) to define problems and solutions that better the lives of citizens, and implement them from concept to production
REQUIREMENTS
  • B.Sc in a technical field (Math, Physics, CS, EE and the likes)
  • Minimum 3 years industry working experience as a hands-on data scientist (beyond academic research)
  • Standard stack (Python’s Numpy, Scipy, Pandas, Scikit)
  • Solid understanding of the basics (dimensionality reduction, SVM, bagging\boosting, outliers)
  • Experience in bringing a project from concept to working prototype, preferably to production
  • Strong coding abilities – production grade code
ADVANTAGES
  • Top-university grad
  • M.Sc or above
  • Research projects in classical ML\DS.
  • AWS\Spark
  • Graph\Network theory
  • Time series forecasting
  • Deep learning (specifically of the time series\LSTM variety)
  • GIS knowledge
  • Outlier detection
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