Survey Methods and Data Science Intern 


The Department 

The Methodology, Analytics, and Data Science (MADS) department at SSRS provides comprehensive methodological and statistical support across the organization. Our team works closely with research teams throughout the project life cycles, delivering high-quality solutions based on experimentally validated methods. We specialize in designing and implementing rigorous, efficient sampling methods, particularly for rare and diverse populations, and employ innovative techniques to weight the resulting survey data. 

In addition to our support role, the MADS department also takes a leadership position in shaping SSRS strategy. We conduct independent research and offer expertise on a wide range of methods for collecting, analyzing, and disseminating public opinion data. Our areas of focus include advanced sample design, such as ABS, RDD, RBS, listed, and other sample sources. We develop protocols to enhance survey response rates and employ adaptive and responsive design to maximize representativeness. Additionally, we specialize in estimating survey error and variance, conducting methodological experiments, exploring new advances in political polling, and applying statistical blending techniques to combine probability and nonprobability sample sources. 

The MADS department also plays a crucial role in maintaining and enhancing the methodological and statistical infrastructure of our probability panel. We ensure the integrity and reliability of the panel data by implementing robust quality control measures, conducting panel recruitment and retention strategies, and implementing advanced techniques for panel sampling, weighting and calibration. Our team continuously monitors and evaluates the panel data to ensure its accuracy and validity, enabling us to provide our clients with reliable and representative insights. 

The Position 

The MADS team is looking for a graduate-level intern to provide support in a variety of statistical and methodological tasks, including survey imputation and weighting, sample design, and other data science enterprises.      We seek candidates who want to join a fast-paced team of researchers, survey methodologists, data scientists, and analysts; who are driven to learn new skills and techniques; who pride themselves on delivering high-quality work; and who can think outside the box to solve challenges; and who can have fun doing it.  

The position will pay $20 an hour, and a hybrid schedule is preferred (Tuesdays and Wednesdays in our Glen Mills, PA headquarters). 

Key Responsibilities 

  • Assist in the design and implementation of rigorous, efficient methods for sampling from diverse populations and weighting resultant survey data; 
  • Prepare and process both structured and unstructured data sets; 
  • Support project teams by providing technical help with sample planning, weighting, and other methodological and survey design issues; 
  • Assist in methodological research and development aimed at improving the representativeness of sample survey data; 
  • Support the development and writing of research papers, professional conference presentations, and journal articles. 

Position Requirements 

  • Current graduate student pursuing degree in statistics, social sciences, or related field. 
  • Coursework in applied mathematics or statistics, quantitative social science, and/or survey research methods. 
  • Skilled in the use of computer software, especially packages for statistical analysis (R, Python, SPSS, SAS, or Stata) and knowledge of computing platforms and data file construction.
  • Meticulous, attentive to detail, and able to collaborate and work well in teams or independently.
  • Willing and able to work across projects and learn new skills quickly in a collaborative and fast-paced environment. 

SSRS is an Equal Opportunity Employer m/f/d/v. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, gender identity or sexual preference.

Equal Opportunity Employer, including disabled and veterans.