Data Science, Finance & Strategy
San Francisco, CA · 全职 · 数据与分析
需要对标的能力
大模型原理 · LLM 基础Agent 工程 · Harness / 脚手架评测 · Eval 方法论多模态 · AI 生图
岗位职责与要求
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
Anthropic's business metrics change quickly. New models, products and pricing land every few weeks, and each one moves the numbers in ways leadership needs to understand and act on quickly.
The Finance Analytics & Business Intelligence team is hiring a senior individual contributor to own how finance leadership reviews key business metrics. You'll run the weekly company metrics update and forum, explain what is driving revenue and usage across products, customers and models, and turn open-ended business questions into clear, data-driven answers. You'll also build what makes those answers repeatable and scalable: governed metric definitions, executive dashboards, and Claude-powered workflows for recurring analysis. The work is technical, fast-moving and highly visible, and it spans Finance, Product, Go-to-Market, Data Science and Data Engineering.
In this role, you will:
• Own the weekly company metrics update and forum: shape the story the numbers tell, evolve the structure and content as fast as the business changes
• Define the metrics that matter: help establish the headline KPIs leadership uses to track the business, and the driver views beneath them.
• Surface macro trends: explain what is driving growth across products, customers, and channels, and flag leading indicators early
• Lead deep dives on executive questions: take a broad question from leadership, put a measurement approach around it, and work with partner teams to deliver a clear, defensible answer
• Track performance against plan: explain where actuals differ from forecast and why it matters
• Partner across teams: work with finance leadership, product and GTM finance and strategy, data science, and data engineering to agree on how we measure the business, so everyone uses the same numbers.
• Raise the bar: land narratives in executive forums and up-level the team’s finance analytics practice by example
You might be a good fit if you:
• Have run an executive metrics forum: you've owned a recurring business review for senior leadership, including its structure, content and discussion.
• Land narratives with executives: your analyses have changed business decisions, and you can simplify for senior leaders without losing rigor
• Put shape around ambiguity: you’ve personally defined the measurement approach for questions nobody knew how to answer, without waiting for a fully specified ask
• Stay hands-on at senior scope: you still write the SQL and Python yourself, and you’d rather ship a defensible v1 with honest error bars than wait for perfect data
• Are inherently curious: you go one level deeper than asked and are energized by how fast models, products, and the market are moving
• Thrive amid shifting priorities: you juggle multiple fast-moving work streams and stay effective when the plan changes weekly
• Work fluently with modern tooling: you’re strong at data visualization, use Claude and AI tools as force multipliers in analysis and BI, and can self-serve your own workflows across SQL, Python, dbt, and a cloud warehouse
Strong candidates may also have:
• Significant experience in finance analytics, business analytics or data science, including direct partnership with senior leadership
• Revenue or growth analytics experience at a usage-based business (cloud, API or marketplace)
• Ownership of a company or executive business review
• Experience designing evals or benchmarks for AI models or products
• Fluency in the LLM model and product landscape
• Experience with a semantic or metrics layer
• Dimensional modeling and warehouse design experience (grain, SCDs, point-in-time correctness)
• Cloud platform experience (AWS, GCP) with orchestration, CI/CD for data, and testing/observability
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:
$270,000—$345,000 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.
For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.
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.
查看中文参考译文
Anthropic 的使命是打造可靠、可解释、可引导的 AI 系统。我们希望 AI 对我们的用户和整个社会都是安全且有益的。我们的团队是一支快速成长的队伍,由专注的研究人员、工程师、政策专家和商业领袖组成,共同构建有益的 AI 系统。
About the role
Anthropic 的 Finance Analytics & Business Intelligence 团队正在招聘一名 senior individual contributor,负责主导我们如何衡量模型价值以及我们在市场中的定位。这些都是没有既定 playbook 的开放性问题:我们的模型每美元、每 token 能创造多少价值,每次发布时这一指标如何变化,我们与前沿领域的其他公司相比如何?
你将主导 Finance 如何量化相对模型价值和市场定位:完善我们的跨产品 benchmark suite,构建能够指导实时定价和 packaging 决策的 task-cost 和 price-elasticity 估算,围绕每次模型 launch 开展 capability 和市场分析,并基于第三方和 survey 数据建立 forecasting。这项工作是开放性的且技术性强,你将作为 analytical lead,与 Product Finance 以及我们的 model performance Data Science 团队紧密合作。
Key responsibilities
· Build the relative-value measurement system: 将我们的跨产品 benchmark 发展为对 model 和 product value 的持久、可信的衡量,涵盖 coding、agentic 和 product-shaped tasks
· Inform pricing and packaging: 构建不同定价产品的 task-cost 近似和 price-elasticity 估算,并将其应用于决策
· Own launch and market analytics: 围绕 model launches 开展分析,包括基于 capability 的 revenue analysis 和更广泛市场的视角
· Deepen our market understanding: 评估并整合外部数据集和研究,以加强我们对市场及其演变的判断
· Partner with Product Finance: 将开放性的定价、packaging 和 positioning 问题从模糊的需求推进到决策级别的答案
· Raise the bar: 在 executive forums 中呈现 narrative,并通过示范提升团队的 product-finance analytics 实践
Minimum qualifications
· Put shape around ambiguity: 你曾亲自为无人知道如何回答的问题定义测量方法,无需等待完整明确的请求
· Land narratives with executives: 你的分析曾改变定价、产品或竞争决策,且能在不牺牲严谨性的前提下为 senior leaders 简化信息
· Stay hands-on at senior scope: 你仍然亲自编写 SQL 和 Python,宁愿交付一个有合理 error bars 的、可辩护的 v1,也不愿等待完美数据
· Are inherently curious: 你会比要求的深入一层,并对模型、产品和市场的快速变化充满热情
· Thrive amid shifting priorities: 你能在多个快速推进的工作流之间灵活切换,并在计划每周变化时保持高效
· Work fluently with modern tooling: 你擅长 data visualization,将 Claude 和 AI 工具作为分析和 BI 中的 force multipliers,并能通过 SQL、Python、dbt 和 cloud warehouse 自助完成工作流
Preferred qualifications
· 设计 AI 模型或产品的 evals 或 benchmarks 的经验
· 大规模定价和 packaging analytics 经验,包括 elasticity estimation
· 从不完美的第三方、panel 或 survey 数据中估算 market share 的经验
· 对 LLM 模型和产品格局的熟悉
· Dimensional modeling 和 warehouse design 经验(grain、SCDs、point-in-time correctness)
· Cloud platform 经验(AWS、GCP),包括 orchestration、data 的 CI/CD 以及 testing/observability
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:
$270,000—$320,000 USD
Logistics
Minimum education: Bachelor’s degree 或同等的教育、培训和/或经验组合
Required field of study: 通过课程、培训或专业经验证明与该 role 相关的领域
Minimum years of experience: 所需经验年限将与该职位的内部 job level 要求相匹配
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.
译文仅供辅助阅读,具体要求以原始招聘来源为准。