Machine Learning Scientist

We're hiring a Machine Learning Scientist to join our Polling and Election Forecasting Team, the group responsible for Blue Rose's flagship electoral products. Our team builds and maintains models that help progressive organizations understand the electoral landscape and make informed decisions. We track public opinion, forecast races across the ballot, develop resource allocation tools, and deliver analysis that campaigns and allied organizations rely on to set strategy.

Our problems are not well suited to off-the-shelf ML solutions: we work with messy, evolving political data and constantly pressure-test our accuracy against real election outcomes. The role spans the full modeling lifecycle from raw survey data to predictive scores and requires both rigorous statistical thinking and the judgment to know when a number is real versus an artifact.

What You'll Do

• Build and maintain end-to-end modeling pipelines, improving reliability, data quality checks, and model diagnostics to increase confidence in our outputs.
• Develop models and perform feature engineering, contributing new ideas to improve our polling methodology. You'll think creatively about the right ways to evaluate model performance in contexts where standard metrics don't apply cleanly.
• Translate statistical outputs into actionable guidance for internal stakeholders, connecting model outputs to the strategic questions campaigns and organizations care about.
• Scope technical needs and ship solutions alongside engineers, statisticians, and political analysts.
• Build subject matter context, developing deep knowledge of the political landscape to inform modeling decisions and catch anomalies.

Preferred Qualifications

• Experience: 4+ years of professional experience in data science, machine learning engineering, or research science with applied statistics and predictive modeling OR equivalent depth from a PhD program. We're open to both paths. What matters is strong instincts for evaluating model performance, diagnosing bugs, and making principled judgment calls under uncertainty.
• Technical: Confident Python / R programmer who can navigate a codebase and implement extensions. Strong data-wrangling experience, ideally including SQL and relational databases.