PDI is one of the largest providers of voter data and campaigning solutions to political campaigns and organizations. PDI has worked with thousands of campaigns and organizations to provide data solutions and campaigning software. PDI is building voter data and software to power the next generation of political organizing, digital, and fundraising tools for campaigns and organizations.
We are seeking an experienced data scientist to join our team as we work to build additional data improvements for our campaigns heading into the coming election cycle and for years to come. The successful candidate will be responsible for using large datasets to create and maintain voter models, analyze voting trends and patterns, and utilize emerging technologies to enhance our voter data products.
This is a remote position reporting to the Director of Data Operations.
The salary range for this role is: $125,000 - $150,000
- 3-5+ years of full-time experience in quantitative data analysis, data science, and development of tools.
- Bachelor’s or Master's degree in a quantitative field such as statistics, data science, computer science, or mathematics.
- At least three years of experience in a data science or analytics role, focusing on large data sets and statistical modeling.
- Knowledge of machine learning techniques and their application in data analysis
- Strong analytical and problem-solving skills, with an ability to work in a fast-paced, dynamic environment
- Excellent communication and collaboration skills, with an ability to effectively present findings and insights to technical and non-technical internal staff
- Experience designing, building, and deploying models.
- Strong knowledge of APIs and API design
- A willingness to dive deep, experiment rapidly, take on new challenges, and get things done.
- Experience working collaboratively in a team and be comfortable communicating complex ideas to both technical and non-technical audiences.
- Demonstrated knowledge of R, Python, or other analytical programming languages.
- Ability to query large data sets using SQL or Spark
- Experience using Data visualization software such as Tableau or Power BI.
- Experience applying statistical and machine learning techniques such as regression, time series forecasting, clustering, optimization, etc.
- Use large and complex data sets to build and maintain models to predict voter behavior, patterns, and trends.
- Conduct statistical analysis and provide insights into voter preferences and tendencies utilizing multiple data sets such as completed surveys, census data, and precinct-level election history.
- Work as a part of a team with technical and business development staff to develop new voter data products and features.
- Present data to internal teams, including how clients can utilize new voter models and data products.
- Continuously monitor and evaluate the effectiveness of voter models and data products, and recommend improvements.
- Lead efforts for statistical modeling, machine learning, and data analysis techniques, using a deep knowledge of the voter landscape and related data sources ranging from US Census Data to county-level election files.
- Develop data and tools for voter analysis, campaign targeting, and a better understanding of likely individual-level voter behavior using programming languages such as Python and R.
- Design and conduct surveys using email-to-web or text-to-web and Survey Monkey to collect additional information on voters in a manner that can be converted to modeling and voter data improvements.
- Build production-quality solutions and work with the infrastructure team to integrate them into business applications.
- Work with the product and development teams to build dashboards that allow clients to analyze data.
- Knowledge of California's political, demographic, and geographic features and how those can be utilized to understand voter data and campaign strategies.
- Experience in the political or civic engagement field, focusing on voter data and analytics.
- Knowledge of the U.S. political landscape and election cycles.
- Experience with geospatial analysis and mapping tools.
- Knowledge of data privacy and security regulations.