Data Scientist, Finance Forecasting (AI)

TL;DR

Data Scientist, Finance Forecasting (AI): Own revenue forecasting models that drive capacity planning and board reporting, build backtesting and accuracy discipline, and lead causal measurement for launches and events with an accent on production time-series forecasting or causal inference. Focus on event-aware architectures, hierarchical reconciliation, and designing repeatable readouts under launch-driven step-changes.

San Francisco, CA or New York City, NY. Location-based hybrid policy: expect to be in one of our offices at least 25% of the time.

Annual Salary: $270,000 - $320,000 USD

Company

hirify.global’s mission is to create reliable, interpretable, and steerable AI systems that are safe and beneficial for users and society.

What you will do

• Own core modeling work: production revenue forecasts (scoping, development, backtesting, deployment, monitoring) or causal measurement for launches and events
• Build and run backtesting and accuracy tracking to improve model quality cycle over cycle
• Contribute to research direction including event-aware forecast architectures, hierarchical reconciliation, and robust causal designs
• Translate model outputs and accuracy into clear recommendations for Finance and executive leadership
• Partner with Analytics Engineer on feature pipelines, model deployment, and forecast store

Requirements

• Substantial experience in data science, forecasting, or quantitative finance, owning models in production
• Deep fluency in Python and SQL, comfortable productionizing builds
• Strong applied statistics foundation with depth in production time-series methods (Prophet, ETS, ARIMA, gradient-boosted, neural forecasting, hierarchical reconciliation) or causal inference (difference-in-differences, synthetic control, Bayesian structural time series, event studies)
• Experience building backtesting and accuracy-tracking discipline, comfortable with public model scoring
• Presented and defended forecasts or causal estimates to executives
• Bias for action in ambiguous, early-stage environments

Nice to have

• Experience with exogenous-regressor or event-aware forecasting
• Familiarity with hybrid or foundation-model forecasting (TimeGPT-class systems)
• Background in pricing, elasticity modeling, or marketing-mix modeling
• Experience forecasting consumption-based or usage-billed businesses (cloud, API, marketplace)

Culture & Benefits

• Competitive compensation, optional equity donation matching, generous vacation and parental leave
• Flexible working hours and lovely office space for collaboration
• Visa sponsorship available (with reasonable effort and immigration lawyer support)
• Collaborative team focused on high-impact AI research as big science
• Location-based hybrid policy with at least 25% office time