Keystone.AI is seeking a Staff Applied Scientist specializing in Causal Inference to help advance their next-generation measurement capabilities. The role will focus on applying and developing scalable causal inference methodologies on large-scale datasets to quantify the impact of interventions and improve strategic planning.
Requirements
- Experience with causal machine learning frameworks and structural causal models.
- Experience with time series methods for causal analysis.
- Proficiency with statistical programming languages such as Python, and experience deploying causal inference algorithms in production settings.
- Experience applying causal inference methods in large-scale, real-world settings (e.g., experimentation in digital platforms, pricing interventions, operational policy changes).
- Familiarity with advanced causal inference techniques such as Bayesian structural time series and uplift modeling.
- Familiarity with time series forecasting, supply chain optimization, or related domains.
Responsibilities
- Design, implement, and productionize causal inference methodologies to quantify the incremental impact of product changes, marketing campaigns, and operational interventions.
- Apply techniques including (but not limited to) double machine learning, instrumental variables, propensity scoring, structural time series, and synthetic control methods.
- Conduct empirical investigations to guide experimentation strategy and develop frameworks for decision-making under uncertainty.
- Contribute to building Keystone's internal libraries for causal estimation and validation.
- Work closely with product managers, economists, data scientists, and ML engineers to integrate causal frameworks into existing workflows, ensuring that model outputs are actionable and aligned with business goals.
- Stay current with research in causal inference and apply novel techniques where appropriate.
- Ensure that causal models are interpretable, statistically valid, and operationally reliable.
Other
- PhD in Economics, Statistics, Computer Science, or a related quantitative field.
- 5+ years of professional experience with applied science, econometric modeling, structural economic analysis, or statistical modeling in practical business applications.
- Familiarity with supply chain processes (such as demand planning, S&OP and inventory optimization) and/or commercial processes (such as marketing measurement, personalization and targeting).
- Excellent communication skills, capable of conveying complex technical concepts clearly to both technical and non-technical stakeholders, including executive-level audiences.
- Demonstrated ability to collaborate with cross-functional stakeholders, including finance, product, and operations, to drive adoption of causal results in strategic decision-making.
