Machine Learning Data Scientist - Consumer Forecasting

Description

The Strategic Finance team at the company shapes its long-term direction. The team works with Product, Engineering and Go-To-Market groups, using data science and economic modeling to support high-stakes decisions.

The company is building a forecasting function within its growing Data Science group. The aim is real-time, data-driven decisions on user growth, revenue, compute infrastructure and other areas.

The company is building scalable forecasting infrastructure to anticipate business dynamics in a usage-based business. These models feed planning, pricing, operational efficiency and growth strategy, and inform major investment decisions.

The company is hiring a senior Machine Learning Data Scientist to lead forecasting work. You will be one of the founding members of the Forecasting pillar and will build robust, interpretable, production-ready forecasting systems that predict metrics such as DAU/WAU, revenue, LTV, compute consumption and profitability.

The role is highly cross-functional. You will work with product managers, researchers, engineers and finance leaders to put forecasts into practice, shape company strategy and build the company's core forecasting capabilities.

The role is based in San Francisco, CA. The work model is hybrid, with 3 days per week in the office. Relocation assistance is offered to new employees.

Responsibilities

• Build statistical and machine learning models for forecasting needs in product, finance, infrastructure and GTM.
• Own the full modeling lifecycle: scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring and explainability.
• Develop and productionize scalable, interpretable forecasts for user growth, monetization, compute load, customer lifetime value and profitability.
• Help build self-service forecasting tools and internal platforms so teams across the company can access and act on real-time predictions.
• Research and assess new forecasting tools and methods, including TimeGPT, large language model extensions, causal forecasting and hybrid approaches.
• Turn technical outputs into business recommendations and decision frameworks.
• Work with cross-functional teams so forecasts are built into planning, experimentation 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 applied data science experience, with hands-on work in forecasting, predictive modeling or marketplace systems.
• Strong grasp of time-series forecasting and the trade-offs between performance, explainability and scalability.
• Proficiency in Python and SQL, plus tools such as scikit-learn, PyTorch or TensorFlow, and forecasting libraries.
• Demonstrated experience monitoring, debugging and maintaining models in production.
• Strong communication and storytelling skills, with the ability to simplify complex topics and influence executives.
• Self-directed and curious, comfortable leading ambiguous projects from 0 to 1.

Conditions

Based in San Francisco, CA.

Hybrid work model with 3 days per week in the office.

Relocation assistance is offered to new employees.

Background checks follow applicable law. Applicants with arrest or conviction records are considered in line with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act for US-based candidates.

The company is an equal opportunity employer and does not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information or other legally protected characteristics.