About EveryAnswer
EveryAnswer is a survey recruitment platform built for researchers and public affairs professionals who need high-quality, representative and hard-to-reach respondents.
We recruit research participants through our digital advertising network built specifically for research quality—enabling research firms to take on projects that would otherwise be infeasible due to geography, incidence rate or audience complexity.
As we grow, we are investing heavily in data science to improve survey analysis, build high-quality reference datasets, and develop predictive models that power our advertising and recruitment engine.
The Role
You will work across survey analysis, geospatial data engineering, advertising analytics, and predictive modeling.
This is a highly technical, execution-focused role that combines statistical rigor, engineering discipline, and applied research thinking.
You will clean and analyze complex datasets, build reproducible pipelines, implement quality control frameworks, and contribute directly to predictive modeling initiatives for advertising cost and performance.
Key Responsibilities
Survey Analysis
- Clean, validate, and preprocess complex survey datasets
- Conduct weighting, imputation, crosstabulation, regression, and MRP analysis
- Apply sampling theory and total survey error principles
- Document methodology clearly for reproducibility
- Deliver accurate, well-structured analysis ready for reporting
Reference and Geospatial Data Engineering
- Build high-quality demographic and geospatial reference datasets
- Normalize messy, fragmented, or inconsistent data sources
- Design deduplication and data validation frameworks
- Work with GIS formats (Shapefile, GeoJSON, KML) and coordinate systems
- Ensure robust data integrity checks and documentation
Advertising Data Analysis
- Analyze large, noisy digital advertising datasets
- Model performance metrics including cost, conversion rates, and targeting efficiency
- Prepare custom audience datasets and evaluate match rates
- Define meaningful KPIs and metrics
- Support reporting and inferential analysis
Modeling & Product Development
- Contribute to predictive models for:
- Advertising cost and conversion rate estimation
- Ad performance feature modeling
- Perform feature engineering
- Apply Bayesian and frequentist statistical approaches as appropriate
- Deploy, monitor, and document models
- Ensure reproducibility and production-readiness
Data Systems
- Develop and maintain ETL pipelines
- Design reproducible analysis workflows in Python and R
- Implement data quality control frameworks
- Reduce analysis errors through improved structure and testing
- Identify automation opportunities and implement scripts or flows
- Improve documentation, schema enforcement, and dataset versioning
What Success Looks Like
Within 6 months, you:
- Independently own standard survey cleaning, weighting, and analysis workflows using and improving existing examples
- Deliver accurate analyses with strong validation and minimal oversight
- Implement at least one reusable, well-documented data pipeline that reduces manual work
- Improve advertising performance reporting through cleaner datasets, clearer KPIs, and structured analysis
- Make tangible progress toward ad performance modeling (feature preparation, exploratory models, documented findings)
- Support high-quality GIS/reference datasets in collaboration with contractors
- Strengthen documentation and reproducibility across data workflows
Core Requirements
- Strong statistical foundation (including Bayesian methods)
- Experience analyzing survey data, including weighting and sampling techniques
- Proficiency in Python and/or R for data analysis
- Experience with pandas/polars, tidyverse, and statistical modeling libraries
- Experience working with SQL databases (Postgres, DuckDB, BigQuery)
- Experience manipulating geospatial datasets (GeoPandas, sf, Shapely, CRS concepts)
- Ability to design ETL pipelines and reproducible workflows
- Experience conducting regression, segmentation, and predictive modeling
- Strong data validation and quality control practices
- Clear documentation and communication skills
- Effective use of AI tools (ChatGPT, Claude, Gemini)
- Experience with Git version control and modern development workflows
- Comfortable working across Python, R, and multiple database systems
Nice to Have
- Experience with MRP modeling
- Experience with PyMC or BRMS
- Experience deploying models to production
- Experience building dashboards (Shiny, Dash, Streamlit)
- Experience designing A/B tests
- Experience analyzing large-scale digital advertising data
- Experience with workflow orchestration tools (e.g., Prefect)
- Experience containerizing services (Docker)
- Experience implementing automated data testing frameworks
How to Apply
If you’re excited about combining statistical rigor, data engineering, and applied modeling to power high-quality research recruitment, we’d love to hear from you.