Data Scientist
San Francisco, CA | New York City, NYFull-TimeMid-levelAI / Data Science
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 part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world.
Core Responsibilities
- Define key metrics, build measurement frameworks, and maintain core reporting to evaluate success
- Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through insights and recommendations
- Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls – and analyze the results in order make actionable recommendations
- Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions
- Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes
- Present complex analyses and recommendations to both technical and non-technical stakeholders
- Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow
You may be a good fit if you have
- 5+ years of experience in data science or analytics roles
- Deep expertise with Python, SQL, and data visualization tools
- Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments
- Highly effective written communication and presentation skills
- A track record of translating complex data into clear, actionable insights for both technical and business stakeholders
- A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress
- A passion for the company’s mission of building helpful, honest, and harmless AI
- Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem
We’re hiring across multiple pillars
- Applying for this role will allow you to be considered for all pillars currently hiring. You will be asked to select a preference when submitting an application.
Platform Product Data Scientist
- You will partner closely with product, engineering, and go-to-market teams to understand how developers and enterprise customers build on and adopt the Claude Developer Platform—spanning our core API, agent orchestration, tool and MCP integrations, and knowledge management capabilities. You'll identify growth opportunities, surface insights about how AI agents are being built and deployed at scale, and drive data-informed decisions that shape our platform roadmap.
- Strong candidates may have:
- 3+ years of experience working closely with Product or Engineering teams on API or developer-facing products, with demonstrated impact on product roadmap and strategy
- Experience supporting B2B sales teams with data insights
- Strong instincts for what drives product adoption, engagement, and retention
Consumer Data Scientist
- You will be embedded with our Consumer product team. This team is responsible for building all consumer-facing Claude experiences—including web, mobile, desktop, and browser extensions. In this role, you'll shape how millions of users interact with Claude daily, driving product insights to product recommendations for interfaces that are intuitive, responsive, and push the boundaries of what AI-powered applications can be.
- Strong candidates may have:
- Experience working closely with Product and Engineering teams on consumer products across multiple platforms (web, iOS, Android, desktop, browser extensions)
- Demonstrated impact on product roadmap and strategy from data deep dives in consumer growth, engagement and retention
- Expert in experimentation, holding a high statistical bar for measuring the impact of core product changes
Research Product Data Scientist
- You will work closely with product, engineering, and research leaders to bring data-driven rigor to every phase of model development and launch. Sitting at the true bleeding edge of putting frontier AI research into the public domain, you will leverage data from both external customers and internal testing to define and measure key company success metrics, and analyze user and model behavior to identify new opportunities to push the frontier.
- Strong candidates may have:
- 3+ years of experience deeply embedding in Product or Research teams, preferably working with LLM or AI products
- Comfort working with unstructured data
- You will be embedded with our Enterprise & Verticals product team - this team is responsible for transforming the way businesses work, enabling enterprise users to know more, create more, and see more with Claude. You'll partner closely with product, engineering, design, sales, and customer success to understand enterprise customers and deliver data-driven insights to improve how Claude serves knowledge workers.
- Strong candidates may have:
- Experience evaluating new verticals and driving data insights to help the team find PMF for 0->1 bets
- Deep understanding over knowledge workers and experience partnering with product teams to build for the Enterprise
Enterprise Marketing Data Scientist
- You will work closely with marketing, product, and commercial teams to define and measure key marketing success metrics, analyze customer acquisition and retention, and build a culture of developing and testing hypotheses through experimentation as we introduce Claude to Enterprise businesses.
- Strong candidates may have:
- 3+ years of experience deeply embedding in Marketing teams, turning marketing data into concise and insightful analysis that drives business outcomes
- Familiarity with both B2C and B2B/Enterprise marketing analytics, and a holistic view of how different marketing programs support one another
- Experience working at multi-segment, multi-product B2B companies serving Enterprise customers
Claude Code Marketing Data Scientist
- You will work closely with marketing, product, and commercial teams to define and measure key marketing success metrics, analyze customer acquisition and retention, and build a culture of developing and testing hypotheses through experimentation as we bring Claude Code to the world.
- Strong candidates may have:
- 3+ years of experience deeply embedding in Marketing teams, turning marketing data into concise and insightful analysis that drives business outcomes
- Familiarity with both B2C and B2B/Enterprise marketing analytics, and a holistic view of how different marketing programs support one another
- Experience building, selling or marketing developer tools
GTM Data Scientist
- This role sits at the intersection of fast-moving sales operations and rigorous statistical analysis. You will work across multiple segments and products, partnering with analytics engineers, fellow data scientists, and go-to-market leadership to turn messy, high-stakes commercial data into actionable strategy. You will play a crucial role in driving data-informed decisions across the commercial customer lifecycle—from new logo acquisition through activation, expansion, and retention—for a rapidly scaling consumption-based AI platform.
- Strong candidates may have:
- A strong track record in multi-segment, multi-product B2B sales or commercial analytics, especially with consumption-based revenue models
Infrastructure Data Scientist
- You'll be at the intersection of data science and infrastructure, using rigorous analysis to understand how platform performance impacts user behavior and identifying high-impact opportunities to improve our systems' reliability and responsiveness. You'll quantify user sensitivity to latency, reliability, errors, and refusal rates, then translate these insights into actionable recommendations that drive meaningful improvements to our platform infrastructure.
- Strong candidates may have:
- Experience with distributed systems and performance engineering, ideally in ML infrastructure contexts (model serving, inference latency, large-scale system metrics)
- Familiarity with SRE practices, error budgets, SLOs/SLIs, observability tools, APM systems, and infrastructure monitoring platforms (e.g., Prometheus, Grafana, DataDog)
Capacity Operations Data Scientist
- You will work closely with infrastructure engineers, product, and finance to understand current utilization patterns, identify optimization opportunities, and build models to forecast future capacity requirements. By ensuring we strategically manage and scale our computing resources to meet research and product needs, your work will be critical to ensuring our infrastructure is efficient, scalable, and ready to support the deployment of safe, frontier AI at scale to the world.
- Strong candidates may have:
- Experience with AI/ML operations & platforms: understanding of API rate limiting, inference workload patterns, accelerator management
- Experience solving resource allocation problems in partnership with Finance or Operations teams
Developer Productivity Data Scientist
- This role sits at the intersection of data science, developer experience, and AI tooling — and offers the rare opportunity to study frontier AI usage from the inside, with the builders themselves as your users. You'll define how Anthropic understands and improves developer productivity — both through classic software engineering effectiveness measures and through the emerging challenge of understanding AI-augmented development workflows. You'll own the quantitative foundation for how Anthropic's engineers build: what slows them down, what accelerates them, where tooling investments pay off, and how AI-assisted development is changing the shape of engineering work. Your analyses will directly inform infrastructure priorities, tooling roadmaps, and how we think about scaling engineering output as Anthropic grows.
- Strong candidates may have:
- Direct experience working with developer productivity, infrastructure, performance, or platform teams in hypergrowth environments
- Deep understanding of distributed systems, cloud infrastructure, and performance engineering, with experience analyzing large-scale system metrics
- 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.
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.
