Machine Learning Scientist

Job Description

Note: We are currently collecting resumes for this role; active hiring will begin in December. Details of the role may be revised when hiring begins.

Blue Rose Research develops cutting-edge products used by the most important progressive organizations in the country. Our research informs short- and long-term strategy for advancing progressive causes and has a trusted track record among key decision makers.

We're a fast, dynamic team shipping innovative work that evolves week by week. We're looking for someone who brings passion for machine learning, sharp attention to detail, and a drive to make an impact – sometimes under tight timelines.

Public examples of our work

• David Shor's conversation with Ezra Klein in the New York Times analyzing the 2024 election is the best public window into how we think.
• How Democrats Will Run on the GOP's Tax Bill: the Democratic Congressional Campaign Committee’s and Democratic Senatorial Campaign Committee’s shared memo on how Blue Rose’s research is informing their campaign communications.
• Bernie Sanders on AI job loss: Senator Sanders cites Blue Rose polling while talking about voters’ fears of AI causing job loss.

What You'll Do

We're hiring a Machine Learning Scientist to join our Scaled Polling & Forecasting Team, the group responsible for Blue Rose's flagship electoral work. 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.

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Specifically, you'll:

• 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 and survey infrastructure. You'll think creatively about the right ways to evaluate model performance in contexts where standard metrics don't apply cleanly.
• Models include Identity Resolution, Record Linkage, and Forecasting models.
• 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 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 programmer who can navigate a codebase and implement extensions. Strong data-wrangling experience, ideally including SQL and relational databases. We are particularly interested in ML expertise in Identity Resolution, Matching / Record Linkage, Survey and Voterfile Modeling, and/or scaled Bayesian Modeling (numpyro/JAX), but all talented ML researchers should apply.
• Collaborative: You thrive in multi-disciplinary teams working alongside engineers, statisticians, and political experts, and you're excited to partner with less technical stakeholders and see your work impact real-world decisions.
• A strong communicator: You can translate between statistical and substantive thinking, explaining what a model is saying (and not saying) to people who think in terms of strategy, not standard errors.
• Curious: You're eager to engage with the wider progressive political ecosystem and develop domain knowledge alongside your technical skills.
• A good teammate: You're a kind person who contributes to a warm working environment.

Location

• This is a remote or in-person optional position. We have an office in New York City and a number of team members who work in-person regularly.
• Our teams work on mostly East Coast hours.

What We Offer

• Salary range: $140,000 – $190,000 annually, commensurate with experience

Candidates must be authorized to work lawfully in the United States.

*Full coverage applies to specific health plans. Different plans may require some level of employee contribution.