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[Remote] Staff Data Scientist, Forecasting in Monetization
Note: The job is a remote job and is open to candidates in USA. Duolingo is on a mission to develop the best education in the world and make it universally available. They are seeking a Staff Data Scientist to own and evolve their critical financial forecasting systems, working closely with product and engineering leadership, finance teams, and senior executives to produce accurate and explainable forecasts.
Responsibilities
- Lead the financial forecasting system. Drive methodology, implementation, maintenance, and delivery as the technical owner of our revenue forecasting stack, in close partnership with the finance team. Publish forecasts that serve as the source of truth for company-wide planning and investor-facing decisions
- Model subscription + experimentation dynamics. Develop modeling architectures that account for experimental lift, cohort behavior, seasonality, and more, making it clear what’s real impact and what’s noise across the hundreds of experiments Duolingo runs each quarter
- Investigate deviations and lead with insight. When forecasts miss, lead the root-cause analysis by quantifying drivers of over- or under-performance (e.g. product changes, user behavior shifts, macro trends), collaborating across functions, and clearly communicating what happened and why to senior leadership. Build and be responsible for the statistics, tools, and dashboards that scale these insights across the company
- Advance the technical frontier. Push our evolution beyond traditional time-series extrapolation and toward more sophisticated approaches such as cohort-based forecasting, retention chaining, and/or causal impact modeling, grounded in business realism and model interpretability
- Mentor and influence. Serve as a technical mentor to data scientists across the organization, set a high standard for modeling, communication, and statistical rigor, and develop roadmaps for forecasting infrastructure and innovation
Skills
- 6+ years of proven experience building production-grade forecasting systems
- Deep applied knowledge of subscription models and understanding of churn dynamics, cohort behaviors, and retention curves
- Advanced degree (Masters or PhD) in Data Science, Economics, Statistics, or a related quantitative field
- Experience assessing and applying theoretical approaches in real-world situations (ex. when to use Bayesian structural models, when causal inference is vital, when a robust heuristic is better than over-fitting noise, etc.)
- Experience distilling uncertainty, nuance, and model tradeoffs into streamlined, actionable narratives for finance and senior leadership partners
- Ability to communicate complex forecasting logic clearly and stand by your work in moments of ambiguity
- Track record of collaboration across technical and non-technical teams
- Consistent record of integrating feedback, aligning across functions, and operating with a strong sense of ownership and service
- Strong judgment when balancing model complexity, interpretability, and speed of development
- Expertise writing clean, well-documented code in Python and SQL
- Building reliable, multipurpose production pipelines
- Past work in organizations where A/B testing is constant, complicating training data and historical baselines
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