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VP of Data Science

ExecuNet Santa Clara Full-Time
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As the lead data scientist, you will be laying out the technological foundations for Company's mission of making self-driving buildings a reality. There is a treasure trove of information waiting to be uncovered and synthesized into insights to drastically improve building efficiency, comfort, conveniences, and safety. It's silly to us that we spend 90% of our time indoors and yet the built environment is one of the last areas to benefit from technological breakthroughs and data-driven solutions; we want you to join us in challenging that status quo. You will be responsible for developing state of the art machine learning, deep learning, and artificial intelligence analytics and tools with Company's unique and rich energy data.

Job Responsibilities:

  • Be a jack of all trades in data: work on all stages of data science projects from understanding the data and the insights they provide to implementing solutions into Company's product offerings.
  • Work closely with the leadership, engineering, and operations teams to develop and integrate solutions into operational platforms, including modern big data processing ecosystems.
  • Provide data modeling, mining, pattern analysis, data visualization, and additional solutions to address business and customer needs.
  • Solving complex industrial problems using advanced ML, DL, AI, and mathematical modeling and optimization techniques with involvement in the whole cycle of data science tasks, including but not limited to: pre-processing, feature engineering, problem formulation, algorithmic selection/evaluation, modeling, hyper-parameter tuning and deployment for the solutions.
  • Perform exploratory data analyses to enrich our models about usage of electrical outlets and identify new questions to pursue.
  • Attend industry conferences to stay current on industry trends, challenges, and potential market opportunities.

Job Qualifications:

  • MS. or PhD or equivalent experience in a quantitative field (i.e. Predictive Analytics, Applied Mathematics, Statistics, Computer Science, Artificial Intelligence, Engineering).
  • Hands on experience with modeling/development software (ie. R, Pandas, Jupyter Notebook, Spark MLlib, TensorFlow).
  • Familiarity with statistical models and methods (i.e. time series analysis, regression analysis, experimental design, hypothesis testing).
  • A track record in building insights and relationships to affect decisions.
  • You have experience engineering information out of massive and complex datasets.
  • You have a high tolerance for ambiguity. You find a way through. You anticipate. You connect, communicate, and synthesize.

Recommended skills

Data Science
Deep Learning
Jupyter Notebook
Mathematical Optimization
Mathematical Modeling
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