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Data Scientist

Palo Alto, California
We are data detectives and our job is difficult because although we have Big Data, we exist in a persistent state of spatio-temporal data starvation. The work is fast-paced, highly iterative, and continuously trading off between what works and what’s best. Along with our team of data scientists, you’ll work with product managers, sales, and software engineers to build data-driven products and solutions. If you enjoy working with data to build products and solve hard problems in creative ways, you will fit right in.

Responsibilities
  • Research and develop algorithms to model real-world systems and behavior based on actual observations
  • Conceive of, and implement, experiments to test hypotheses regarding (statistical) behavior of data and algorithms
  • Translate product management, engineering, and business constraints/queries into tractable data science questions
  • Work cross-functionally with software engineers to integrate data science models into production pipelines
  • Help develop, improve, and evangelize our data science knowledge base and infrastructure
  • Iterate rapidly on everything; all of the above happens in a relatively fast paced business driven environment, and you must be comfortable with that
 
Qualifications
  • US Citizen required (clearance eligible)
  • Masters degree in a STEM field (e.g., statistics, machine learning, computer science, physics, engineering)
  • An ability to communicate complex data science concepts and results in a readily-understood manner
  • Two years of demonstrable experience working with large and noisy datasets, and particular experience with datasets that have nevertheless have sparsity in some dimension (e.g., spatially, temporally) would be a big plus
  • Demonstrable experience implementing efficient data science models and deploying them in a production environment
  • Excellent coding skills (python with a solid familiarity with NumPy, SciPy, pandas strongly preferred)
  • Experience with git, GitHub, SQL, AWS (S3 and EC2), Docker, and Unix/Linux a big plus
 projects all the way from prototype to production-ready.
 

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