Data Science, Finance & Strategy

About this role

<div class="content-intro"><h2><strong>About Anthropic</strong></h2> <p>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.</p></div><h2 class="heading"><strong>About the role</strong></h2> <p>Anthropic’s Finance Analytics & Business Intelligence team is hiring a senior individual contributor to own how we measure the value of our models and our position in the market. These are open questions without an established playbook: how much value do our models deliver per dollar and per token, how is that changing with every launch, how do we compare to the rest of the frontier?</p> <p>You’ll own how Finance quantifies relative model value and market position: maturing our cross-product benchmark suite, building task-cost and price-elasticity estimates that inform live pricing and packaging decisions, sourcing and running capability and market analysis around every model launch, and standing up forecasting on third-party and survey data. The work is open-ended and technical, and you’ll operate as an analytical lead, partnering closely with Product Finance and our model performance Data Science teams.</p> <h2 class="heading"><strong>Key responsibilities</strong></h2> <ul> <li><strong>Build the relative-value measurement system: </strong>evolve our cross-product benchmark into a durable, trusted read on model and product value, spanning coding, agentic, and product-shaped tasks</li> <li><strong>Inform pricing and packaging: </strong>construct task-cost approximations and price-elasticity estimates across differently priced products, and carry them into decisions</li> <li><strong>Own launch and market analytics: </strong>run analytics around model launches, including capability-based revenue analyses and views of the broader market</li> <li><strong>Deepen our market understanding: </strong>evaluate and integrate external datasets and research to strengthen our read on the market and how it's evolving</li> <li><strong>Partner with Product Finance: </strong>take open-ended pricing, packaging, and positioning questions from vague ask to decision-grade answer</li> <li><strong>Raise the bar: </strong>land narratives in executive forums and uplevel the team’s product-finance analytics practice by example</li> </ul> <h2 class="heading"><strong>Minimum qualifications</strong></h2> <ul> <li><strong>Put shape around ambiguity: </strong>you’ve personally defined the measurement approach for questions nobody knew how to answer, without waiting for a fully specified ask</li> <li><strong>Land narratives with executives: </strong>your analyses have changed pricing, product, or competitive decisions, and you can simplify for senior leaders without losing rigor</li> <li><strong>Stay hands-on at senior scope: </strong>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</li> <li><strong>Are inherently curious: </strong>you go one level deeper than asked and are energized by how fast models, products, and the market are moving</li> <li><strong>Thrive amid shifting priorities: </strong>you juggle multiple fast-moving workstreams and stay effective when the plan changes weekly</li> <li><strong>Work fluently with modern tooling: </strong>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</li> </ul> <h2 class="heading"><strong>Preferred qualifications</strong></h2> <ul> <li>Experience designing evals or benchmarks for AI models or products</li> <li>Pricing and packaging analytics at scale, including elasticity estimation</li> <li>Market share estimation from imperfect third-party, panel, or survey data</li> <li>Fluency in the LLM model and product landscape</li> <li>Dimensional modeling and warehouse design experience (grain, SCDs, point-in-time correctness)</li> <li>Cloud platform experience (AWS, GCP) with orchestration, CI/CD for data, and testing/observability</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p>The annual compensation range for this role is listed below. </p> <p>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.</p></div><div class="title">Annual Salary:</div><div class="pay-range"><span>$270,000</span><span class="divider">—</span><span>$320,000 USD</span></div></div></div><div class="content-conclusion"><h2><strong>Logistics</strong></h2> <p><strong>Minimum education: </strong>Bachelor’s degree or an equivalent combination of education, training, and/or experience</p> <p><strong>Required field of study: </strong>A…