About this Data Science, Finance & Strategy role at Anthropic
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 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?
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.
Key responsibilities
- Build the relative-value measurement system: evolve our cross-product benchmark into a durable, trusted read on model and product value, spanning coding, agentic, and product-shaped tasks
- Inform pricing and packaging: construct task-cost approximations and price-elasticity estimates across differently priced products, and carry them into decisions
- Own launch and market analytics: run analytics around model launches, including capability-based revenue analyses and views of the broader market
- Deepen our market understanding: evaluate and integrate external datasets and research to strengthen our read on the market and how it's evolving
- Partner with Product Finance: take open-ended pricing, packaging, and positioning questions from vague ask to decision-grade answer
- Raise the bar: land narratives in executive forums and uplevel the team’s product-finance analytics practice by example
Minimum qualifications
- 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
- Land narratives with executives: your analyses have changed pricing, product, or competitive decisions, and you can simplify for senior leaders without losing rigor
- 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 workstreams 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
Preferred qualifications
- Experience designing evals or benchmarks for AI models or products
- Pricing and packaging analytics at scale, including elasticity estimation
- Market share estimation from imperfect third-party, panel, or survey data
- Fluency in the LLM model and product landscape
- 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.
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.
