Leads forecasting initiatives by building interpretable, production-ready statistical and machine learning models for growth, revenue, compute, and profitability. Requires an advanced quantitative degree, 7+ years of applied data science experience, and strong expertise in time-series forecasting.
230k – 385k/yr
Hybrid7+ YOEData Science
About the role
Responsibilities
Build statistical and machine learning models for forecasting needs across product, finance, infrastructure, and go-to-market domains.
Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development, experimentation, deployment, monitoring, and explainability.
Develop and productionize scalable, interpretable forecasts for user growth, monetization, compute load, customer lifetime value, and profitability.
Contribute to self-service forecasting tools and internal platforms that enable teams to access and act on real-time predictions.
Research and evaluate forecasting techniques and tools, including TimeGPT, large language model extensions, causal forecasting, and hybrid approaches.
Translate technical outputs into business-aligned recommendations and decision frameworks.
Collaborate with cross-functional teams to integrate forecasts into planning processes, experimentation workflows, and executive decision-making.
Requirements
Advanced degree (MS or PhD) in a quantitative field such as Statistics, Computer Science, Economics, or Operations Research.
7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems.
Expertise in time-series forecasting techniques and practical understanding of trade-offs among performance, explainability, and scalability.
Proficiency in Python, SQL, scikit-learn, PyTorch or TensorFlow, and forecasting libraries.
Experience with model monitoring, debugging, and long-term maintenance in production environments.
Strong communication and storytelling skills, with the ability to simplify complexity and influence executive stakeholders.
Ability to lead ambiguous projects from 0 to 1.
Nice-to-haves
Experience building or scaling forecasting platforms in a high-growth company.
Familiarity with causal inference and Bayesian forecasting.
Passion for AI and a strong perspective on how machine learning should inform strategic decisions.
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