Interested in this Data Scientist role at Priorities USA?
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About This Role
About Priorities USA
Priorities USA runs effective, large-scale, digital voter mobilization and persuasion campaigns in key battleground states. We invest in cutting-edge research and data. We build training and tools to grow our collective, progressive power. In partnership with movement allies, we employ innovative digital strategies and empower voters.
Position Summary
We are looking for a data science contractor who will support R&D efforts to prototype Priorities USA’s next generation of digital intelligence tools in addition to experimentation on new data methods to be applied to digital campaigns. This role will sit at the intersection of data science and software engineering, requiring a strong intuition for understanding and applying data & statistical methods as well as the ability to maintain rigorous shared software development standards within a team.
This person will be involved with prototyping, evaluating, and implementing methods and features geared at helping digital media practitioners run more efficient and effective campaigns. The role is ideal for someone with a strong foundation in data science, who is excited by innovation, and is eager to apply those skills in service of progressive causes. The contractor will work directly with the Senior Director of R&D.
This is a remote engagement. Contractors must be based in the United States and will provide their own equipment. We expect availability for regular check-ins during Eastern Time business hours. Scope of Work* Explore, prototype, and validate new analytical methods and LLM applications across a range of political, media, and advertising data contexts.
- Build proof-of-concept tools, features, and datasets, and iterate on them based on feedback from digital practitioners.
- Engineer validated methods into modular, sturdy, well-documented pipelines and applications that are ready to graduate into production systems.
- Develop software primarily through agentic development workflows, producing well-specified, reviewable, tested, and documented code.
- Think critically about what data is saying and communicate findings, tradeoffs, and recommendations clearly to technical and non-technical audiences.
Qualifications* Strong programming skills in SQL and Python.
- Demonstrated ability to apply statistical and data science methods in real-world contexts.
- Strong software engineering habits (version control, code review, testing, documentation).
- Hands-on experience applying LLMs in real projects — e.g., prompt and context engineering, classification or extraction pipelines, evaluation of model outputs, or agentic frameworks.
- Experience working with agentic software development tools (e.g., Cursor, Claude Code, Codex).
- Experience working with GCP (and preferably AWS), including building and maintaining data pipelines.
- Experience using git + GitHub
- Familiarity with cybersecurity best practices
- Strong communication skills and the ability to collaborate across teams with different working styles and levels of technical experience.
- Demonstrated curiosity and self-directed exploration through side projects, prototypes, experiments, or other evidence that you build and try things on your own initiative.
- Experience communicating with diverse groups, working in a multicultural workforce, and bringing a sensitivity and appreciation of cultural differences
Preferred Qualifications* Experience working with digital advertising, media performance, social listening, or media consumption data.
- Experience working with political, advocacy, or campaign data such as voter files.
- Experience with containerization and orchestration.
- Experience developing front-end applications or dashboards.
- Experience building evaluation frameworks or benchmarks for LLM or ML systems.
Compensation
Compensation for this engagement is $125–175 per hour, commensurate with experience.
Equal Opportunity Employer
People of color, women, people with disabilities, and veterans are strongly encouraged to apply. If you need an accommodation for a disability to apply to this position, please let us know by emailing [email protected].
Application Information
Applications will be accepted until the role is filled. Applications should include a resume and a link to a GitHub profile or relevant work samples; a cover letter is optional. The process for selected candidates will consist of a compensated take-home exercise and two rounds of interviews with members of the team. We are aiming for a start date in mid-October.
This engagement runs through December 31, 2026.
Salary Context
This $260K-$364K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At Priorities USA, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($312K) sits 64% above the category median. Disclosed range: $260K to $364K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Priorities USA AI Hiring
Priorities USA has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $364K - $364K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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