Pay
Higher than most similar jobs
$270K–$345K against $118K–$193K, the middle half of what similar US postings state.
From 3,721 postings in the last year. Stated ranges, not salaries paid.
Data Scientist, Finance Forecasting
San Francisco, CA
Actively hiring, first seen by us today- Pay
USD 270,000–345,000/year — pay source
Annual Salary: $270,000—$345,000 USD Logistics
The annual compensation range for this role is listed below.
Read the full posting- Work setting
- Not stated
- Employment
- Not stated
Before you apply
- Work authorization
- Sponsors visas
What you’ll work on
Full postingAs an early member of our Finance Analytics and Business Intelligence team, you will play an instrumental role in our company's mission of building safe and beneficial artificial intelligence by establishing robust analytics engineering and business intelligence capabilities for our Finance & Accounting organization.
We're hiring for two seats on this team, and we expect candidates to lead with depth in either production time-series forecasting or causal inference.
Own revenue forecast models from scoping through deployment and monitoring
Build and run backtesting and accuracy tracking to improve forecast quality
Lead causal measurement of how launches and events moved revenue
Translate model output and accuracy results into recommendations for Finance and executives
Partner with the Analytics Engineer on feature pipelines, deployment, and the forecast store
From the employer’s posting
As an early member of our Finance Analytics and Business Intelligence team, you will play an instrumental role in our company's mission of building safe and beneficial artificial intelligence by establishing robust analytics engineering and business intelligence capabilities for our Finance & Accounting organization. In this unique company, technology, and moment in history, your work will be critical to ensuring accurate financial reporting, streamlining accounting processes, and supporting our financial operations as we deploy safe, frontier AI at scale to the world.
We're hiring for two seats on this team, and we expect candidates to lead with depth in either production time-series forecasting or causal inference. Both shapes are first-class; tell us in your application which one feels more like you.
you'll own revenue forecasting models that drive capacity planning and board reporting
Build and run backtesting and accuracy tracking for your models, and use the results to improve quality cycle over cycle
lead causal measurement work that tells us how much a given launch or event actually moved revenue
Translate model output and accuracy results into clear recommendations for Finance and executive leadership
Partner with the team's Analytics Engineer on feature pipelines, model deployment, and the forecast store
What you’ll bring
All qualificationsPreferred experience
- Experience with exogenous-regressor or event-aware forecasting approaches
- Experience forecasting a consumption-based or usage-billed business (cloud, API, marketplace)
- Familiarity with hybrid or foundation-model forecasting (TimeGPT-class systems)
Qualification wording
Experience with exogenous-regressor or event-aware forecasting approaches
Experience forecasting a consumption-based or usage-billed business (cloud, API, marketplace)
Familiarity with hybrid or foundation-model forecasting (TimeGPT-class systems)
Education & alternatives
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
About Anthropic
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems.
In the employer’s words · Read in context
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 4, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 7 and day 55.
From 10,000 similar postings that came down. Coming down is not always a hire.
Anthropic is hiring
28 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Scientist hiring has slowed
- Takes jobs down at a usual pace
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
Where the posting names them
- Have substantial experience in data science, forecasting, or quantitative finance, including time owning models in production rather than only in notebooks - Are deeply fluent in Python and SQL and comfortable productionizing what you build - Have a strong applied statistics foundation, with depth in either production time-series methods (Prophet, ETS, ARIMA, gradient-boosted approaches, neural forecasting, hierarchical reconciliation) or causal inference (difference-in-differences, synthetic control, Bayesian structural time series, event studies)
Your next step
Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job description
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 an early member of our Finance Analytics and Business Intelligence team, you will play an instrumental role in our company's mission of building safe and beneficial artificial intelligence by establishing robust analytics engineering and business intelligence capabilities for our Finance & Accounting organization. In this unique company, technology, and moment in history, your work will be critical to ensuring accurate financial reporting, streamlining accounting processes, and supporting our financial operations as we deploy safe, frontier AI at scale to the world.
As a founding Data Scientist on Anthropic's Finance Forecasting team, you'll own revenue forecasting models that drive capacity planning and board reporting, build the backtesting and accuracy discipline that makes those forecasts trustworthy, and lead causal measurement work that tells us how much a given launch or event actually moved revenue — turning what is currently a best-guess conversation into a designed, repeatable readout.
We're hiring for two seats on this team, and we expect candidates to lead with depth in either production time-series forecasting or causal inference. Both shapes are first-class; tell us in your application which one feels more like you.
This is a build role on a small team. The forecast is visible inside the company, and when it's wrong, people notice. We're looking for someone who would rather have their models scored than stay vague, and who is excited to set the bar for how forecasting is done here.
Key responsibilities
-
Own a core piece of the team's modeling work — either the production revenue forecasts themselves (scoping, development, backtesting, deployment, monitoring) or the causal measurement program for launches and events
-
Build and run backtesting and accuracy tracking for your models, and use the results to improve quality cycle over cycle
-
Contribute to the team's broader research direction, including event-aware forecast architectures, hierarchical reconciliation, and causal designs that hold up under launch-driven step-changes
-
Translate model output and accuracy results into clear recommendations for Finance and executive leadership
-
Partner with the team's Analytics Engineer on feature pipelines, model deployment, and the forecast store
Minimum qualifications
-
Have substantial experience in data science, forecasting, or quantitative finance, including time owning models in production rather than only in notebooks
-
Are deeply fluent in Python and SQL and comfortable productionizing what you build
-
Have a strong applied statistics foundation, with depth in either production time-series methods (Prophet, ETS, ARIMA, gradient-boosted approaches, neural forecasting, hierarchical reconciliation) or causal inference (difference-in-differences, synthetic control, Bayesian structural time series, event studies)
-
Have built backtesting and accuracy-tracking discipline before and are comfortable having your models scored publicly
-
Have presented and defended a forecast or causal estimate to executives
-
Have a bias for action and do your best work in ambiguous, early-stage environments
Preferred qualifications
-
Experience with exogenous-regressor or event-aware forecasting approaches
-
Familiarity with hybrid or foundation-model forecasting (TimeGPT-class systems)
-
Background in pricing and elasticity modeling or marketing-mix modeling
-
Experience forecasting a consumption-based or usage-billed business (cloud, API, marketplace)
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.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Annual Salary: $270,000—$345,000 USD Logistics
More source context
The annual compensation range for this role is listed below.
- Location & working pattern
San Francisco, CA
- Experience with exogenous-regressor or event-aware forecasting approaches - Familiarity with hybrid or foundation-model forecasting (TimeGPT-class systems) - Background in pricing and elasticity modeling or marketing-mix modeling
More source context
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.
- Work authorization
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.