Team Member Candid Photo

Mark is Balance’s Lead Data Scientist. He has more than six years practicing as both a Data Scientist and Data Science Instructor with a focus on ecological modeling and ecosystem regeneration. Mark has collaborated with Balance hydrologists to develop climate aware models for evaluating restoration efforts in California watersheds, train predictive models on flooding events at Bay Area gauging sites, and consolidate decades of historical data for easier analysis.

Mark continues a lifelong vocation of applying scientific knowledge and methodologies to confronting climate change. At Balance, Mark provides internal support for data reporting and has developed data modeling projects with colleagues and clients.

Mark graduated with a PhD research in Materials Science & Engineering from UC San Diego and a Bachelor’s of Science in Engineering Physics from UC Berkeley studying carbon capture and conversion methods with renewable energy. Mark transitioned to Data Science in 2017 and an instructor at Galvanize Data Science Immersive in 2018 was a founding instructor at the Los Angeles campus in 2020.

  • Bachelor’s of Science in Engineering Physics, University of California at Berkeley, 2007
  • PhD Materials Science & Engineering, University of California at San Diego, 2016

    • AutoCAD, GIS Analysis, Geomorphology, Grant Strategies and Management, Groundwater, Groundwater and Surface Water Interactions, HEC-HMS, Habitat Restoration, Hydrology, Illustration, Outreach, Stormwater, Stream Gaging, Water Policy

    Key Projects

    • Perazzo Meadows and Lacey Meadows Machine Learning Studies, Mark has evaluated the predictive strength of publicly available climate data and Balance streamflow data for late season water retention and streamflow in the larger Lacey Meadows and Perazzo Meadows system. Mark continues to develop methodologies that use climate data models to estimate improvement in retention and flow from future restoration efforts in these systems.
    • San Mateo County Flood Warning System, Mark is creating time-series machine learning models and deep learning models with data from San Mateo County gauging sites to predict flooding events with maximal warning lead time.
    • Data Archive, Mark is organizing company site data and building database infrastructure for internal company usage and building data resilience and accessibility for both internal use and for clients with particular data needs.

    Few things are quite as fulfilling as witnessing the moment an audience member grasps a complex concept or realizes the importance of a result for themselves.

    Lake Merced, San Francisco, California

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