Urban Research Corps
The below position(s) are currently available in the Urban Research Corps. Please submit your materials via the Google Form linked below.
Civic Innovation Research Engineer
Deadline to apply: March 15, 2021
The Civic Innovation project is part of a National Science Foundation planning process that aims to measure spatial accessibility in neighborhoods. Research activities will consist of testing available methods, building a technical prototype, and comparing geospatial data on local amenities. The objective of this project is to forge a new approach towards measuring spatial accessibility, centered around community priorities and that leverages advances in data and computation.
Million Neighborhoods Research Engineer
Deadline to apply: March 15, 2021
The Million Neighborhoods Initiative is a project to systematically map the world’s slums and develop tools to help accelerate community-driven planning focused on solutions to lack of services. This position will focus on working with the Million Neighborhoods codebase to refine and expand its capabilities, develop novel analyses using the data, and prototype interactive tools and improvements to the data pipeline.
The roles are up to 15 hours per week starting Winter Quarter through Spring 2021.
- Fluency in Python or R.
- Seeking a graduate degree (e.g., Master’s or PhD).
- Experience developing pipelines that involve data ingestion, standardization, and transformation.
- Experience applying machine learning methods that generalize real-world processes.
- Experience with structured, semi-structured, and/or unstructured data formats (e.g., CSV, JSON, Simple Feature, raster/GeoTIFF, and/or XML).
- Knowledge of software development practices, such as: version control; package management / virtual environments (e.g., pyenv, poetry, PipEnv, conda); unit testing; code review.
- Experience with Linux-based systems and command line tools such as Bash.
- Eagerness to constantly learn, ask questions, share knowledge, and teach others.
- Empathetic and self-aware mindset; express mutual respect, trust, and willingness to assist team members.
- Ability to work in a dynamic environment where requirements and solutions evolve through collaboration.
- Ability to articulate technical barriers and proactively solicit help from team members.
- Ability to work independently on the design and development of research methods.
- Ability to scope the feasibility of unstructured research tasks; self-delegate and create personalized work plans; identify, evaluate, and apply existing solutions to avoid “reinventing-the-wheel.”
- Capable of autodidactic learning from academic articles, self-guided tutorials, Stack Overflow, and package documentation.
- A Bachelor’s degree in a technical subject area is not required, but some formal training in math, computer science, statistics, economics, physics, engineering, or social science is strongly preferred.
- Significant experience in Python, R, and SQL.
- Experience with ETL and data ingestion through web scrapes, S3/FTP sync, and/or APIs.
- Experience writing clean and readable documentation for code repositories and datasets.
- Experience working in the tech industry, applied research organizations, as part of a cross-functional software development team, or working with a principal investigator.
- Familiarity with PyTorch, TensorFlow, Keras, TensorFlow Probability, and/or PyMC3. Practical understanding of probability and statistics, including Bayesian inference.
- Familiarity with workflow management platforms such as Apache Airflow, dbt (data build tool), or large-scale batch and streaming processing frameworks like Apache Beam or Google Cloud Dataflow.
- Familiarity with Google Cloud Platform, Amazon Web Services, Microsoft Azure, Midway HPC / SLURM.
- Familiarity with cloud deployment frameworks (e.g., Docker, Google Cloud Deployment Manager, AWS CloudFormation, Azure Resource Manager, Serverless Framework, TerraForm, Heroku).
Class Level Eligibility:
All current graduate students.
How to Apply
Participants must complete a short online questionnaire, submit their resume, and provide a link to a previous project that includes a code sample. The interview process involves a 30-minute conversation and a short take-home technical assessment.
To apply, please submit the following materials:
- Google Form Q&A
- PDF Resume
- Example of previous project work that includes code