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Lead/Senior Data Scientist

 

About Sense 360:

Sense360 is big-data start-up that is transforming how companies make strategic decisions.  We work with some of the top restaurants, retailers, agencies and tech companies in the world providing them access to our revolutionary data-set and dashboard. We are funded by the same VCs as Uber, Pinterest, and Riot Games and were founded by successful repeat entrepreneurs.

 

About Our Culture:

Our company culture is a consequence of our strong data team; from the ground up we are built on data. The products our Data Science team synthesizes are central to Sense360’s strategy and success. We emphasize ownership throughout the entire product development lifecycle: our data scientists are not only expected to generate analytical solutions, but to also convert these solutions into stable scalable production implementations. We love new technology, and solving many of our biggest issues require bleeding edge work.

 

About the Role:

We are looking for tenacious, highly engaged machine learning experts that are comfortable developing algorithms and implementing them at scale.  In this role, you will be spearheading the development of fundamental solutions for our product by continuously improving our core machine learning algorithms.  

 

Responsibilities:

  • Sift through terabytes of data to formulate and test algorithmic changes to our data pipelines.
  • Employ techniques ranging from multi-class sequential labeling to semi-supervised learning on our vast quantity of data.
  • Use Spark in an ad-hoc manner to assemble training sets. 
  • Research and develop improved scoring frameworks for our pressing challenges in a data-friendly platform (either R or Python).
  • Work in conjunction with Engineering and Product to ensure smooth roll out of algorithms to production environments.

You Have:

  • An Advanced Degree with Ph.D. in Computer Science, Computational Biology, Applied and Pure Mathematics, and Physics preferred (other quantitative disciplines also considered).
  • Extensive experience driving research programs in machine learning and statistics.
  • The ability to learn, evaluate, and implement emerging technologies quickly.
  • Experience in R or Python’s standard data and machine learning toolsets.
  • Familiarity with standard software engineering best practices and experience implementing models for automated scoring.
  • A strong understanding of the Apache Spark/Hadoop ecosystem.
  • Experience in a wide variety of deep learning model types and their applications.
  • A true “nose” for data - you can quickly sniff out and follow leads in our data, and turn these leads into ideas on how to improve our system.
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