Our recent projects include cutting-edge COVID-19 risk analysis using dynamic smart thermometer and movement data, housing market and eviction analysis, and the development of dynamic indices to support agencies and relief providers in Louisiana. We are also supporting multiple state and local agencies in land use, transit, and climate planning.
Building new spatial and spatiotemporal data products and models including quantitative risk indices from our unique combination of public and private socio-demographic, health, movement, and built environment dataYour responsibilities will include:
- Improving our hierarchical land use classification algorithm and related downscaling methodologies through the application of geostatistical and machine learning models
- Owning data science problems end-to-end, from ideation, exploratory data analysis and prototype, through to production implementation
- Communicating results with business stakeholders including model accuracy, business risk associated with data products and potential customer impacts of model improvements
Your background most likely includes:
- A Master’s degree or higher in Statistics or Data Science, Computer Science, Economics, Biostatistics or Public Health or Epidemiology (with an emphasis on geospatial or spatiotemporal modeling), or other similar technical fields
- Practical experience with a variety of statistical and machine learning models including classical statistical and machine learning classification models (multinomial logistic regression, SVM, and tree-based methods), hierarchical modeling (both classification and regression) and clustering algorithms
- Fluency in Python and Python’s scientific programming stack including pandas, GeoPandas, sklearn, and various visualization packages including map-based visualizations
- Experience developing data products and/or models in an iterative, fast-paced environment
- Experience providing technical mentoring of other data scientists
- Excellent communication, collaboration, and documentation skills
- Experience as a full-stack data scientist, owning data pipelines, model development and production model implementation
- Experience working with publicly available socio-demographic data, including US Census data and/or publicly available location data such as OpenStreetMap
- Familiarity with a variety of large-scale data analysis tools including SQL and PySpark
- Passion for urban planning, climate resilience, equity, and leveraging data to facilitate a more equitable and resilient society
If you are creative, pragmatic and have practical experience with spatiotemporal data and statistical and machine learning models, we’d like to talk to you about joining our team!
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