Asks for

The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”
The ad says“Proficiency in Python, SQL, and data analysis tools, with experience working with external and public data sources”
The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”
The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”
The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”
The ad says“Proficiency in Python, SQL, and data analysis tools, with experience working with external and public data sources”
The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”
The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”
The ad says“Proficiency in Python, SQL, and data analysis tools, with experience working with external and public data sources”
The ad says“Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data”

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

As one of our first Data Scientists dedicated to policy work, you will play a key role in ensuring Anthropic's work is understood by policymakers around the world. You'll sit at the intersection of data science and public affairs, transforming internal product usage and survey data into clear, accurate, and consistent evidence the Policy team can use to inform its positions, demonstrate Anthropic’s relevance to policymakers, and measure the impact of its work.

Your analyses will directly inform how legislators, regulators, and the public understand Anthropic's footprint and contribution. This is a highly cross-functional role that is foundational to Anthropic's mission of bringing powerful AI to the world in a way that benefits humanity.

Key responsibilities

Partner with product and business teams across the company to produce supporting analyses and data collateral for specific policy position papers and partnership conversations
Determine which metrics faithfully represent our company to legislators, regulators, and the public
Own the dashboards and pipelines that keep externally-shared numbers consistent, so the Policy team can move quickly without creating discrepancies
Use AI tools to aggregate news, policy developments, and other public data sources to track trends and provide a clear picture of the evolving regulatory landscape
Develop measurement frameworks for policy communications, paid media, and public-affairs campaigns
Design and implement policy analyses to support internal position development or measure the impact of policy implementation for external audiences

Minimum qualifications

Proficiency in Python, SQL, and data analysis tools, with experience working with external and public data sources
Experience producing analysis that reaches external audiences such as policy, communications, investor relations, public affairs, or published research
Applied causal inference using quasi-experimental designs (e.g., difference-in-differences, regression discontinuity, synthetic control, instrumental variables, or matching) to measure policy or program impact from observational data
Demonstrated ability to translate complex analyses into clear, actionable recommendations for audiences with differing levels of technical fluency

Preferred qualifications

6+ years of hands-on data science experience
Direct experience supporting a policy, government affairs, or regulatory team with data and analysis
Familiarity with investor-relations or financial-disclosure data standards and the consistency requirements they entail
Comfort operating in ambiguous, fast-moving environments where creating clarity and driving progress is part of the role
A genuine interest in Anthropic's mission of building safe and beneficial AI

Deadline to apply: None. Applications will be accepted on a rolling basis.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$285,000—$380,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.