Machine Learning Scientist

Blue Rose Research; United States; Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401k match

20 weeks parental leave

Fertility and family-building support

Job summary

Blue Rose Research is hiring a Machine Learning Scientist to join the Scaled Polling & Forecasting Team. You’ll build and maintain models that track public opinion, forecast races, and inform strategic decisions for progressive campaigns.

The role emphasizes end-to-end modeling, feature engineering, and translating complex outputs into actionable guidance for internal stakeholders. Remote or NYC office; East Coast hours; strong Python and data-science skills required.

Qualifications

• 4+ years in ML engineering or research with applied statistics and predictive modeling OR PhD depth.
• Strong Python programming and data wrangling (SQL).Experience with Identity Resolution, Record Linkage, Survey and Voterfile Modeling, or scaled Bayesian Modeling.

Responsibilities

• Build and maintain end-to-end modeling pipelines.
• Develop models and perform feature engineering.
• Translate statistical outputs into actionable guidance for stakeholders.
• Scope technical needs and ship solutions with engineers and analysts.
• Build subject matter context to inform modeling decisions.

Skills

Python

SQL

Identity Resolution

Record Linkage

Survey Modeling

Voterfile Modeling

Bayesian Modeling

NumPyro

JAX

Education

PhD or equivalent depth in ML/Statistics

Tools

NumPyro

JAX

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.

• 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.

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.

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
• We offer medical, dental, vision, 401k match, 20 weeks paid parental leave, fertility and family-building support through Maven Clinic, and unlimited PTO.

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.