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Company background
Keystone.AI is a premier artificial intelligence, economics, and technology firm providing AI-driven services to large companies, law firms, and government agencies. The firm builds and deploys enterprise AI solutions at-scale that automate and optimize operational and commercial decision-making. We are all connected by one mission: bringing transformative ideas to life on behalf of our clients. Keystone’s Core AI group employs the world’s best AI/ML science and technology practitioners with unparalleled experience implementing highly complex, massive-scale algorithms and models to help companies make better decisions across manufacturing, supply chain management, sales, and marketing.
Position Overview
Keystone.AI is seeking a Staff Applied Scientist specializing in Causal Inference to help advance our next-generation measurement capabilities. Our team builds scientific systems that power decision-making across industries, enabling clients to evaluate and optimize their most critical initiatives through rigorous causal analysis. This 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.In this role, you will work at the frontier of applied causal science, leveraging advanced techniques such as double machine learning, structural time series analysis for causal inference, and synthetic controls. You will contribute to the design and deployment of robust causal measurement pipelines and collaborate closely with product teams, economists, and engineers to translate scientific innovations into business value.
If you're passionate about advancing causal inference from theory to practice, and eager to shape real-world outcomes with scientific rigor, we invite you to apply. Experience deploying causal models at scale or integrating them into business processes is highly valued.
What You’ll Do
- Develop Scalable Causal Models: 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.
- Advance Scientific Rigor: 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.
- Collaborate Cross-Functionally: 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.
- Contribute to Methodological Innovation: Stay current with research in causal inference and apply novel techniques where appropriate. Participate in internal knowledge sharing and contribute to the broader scientific direction of the team.
- Promote Model Integrity and Interpretability: Ensure that causal models are interpretable, statistically valid, and operationally reliable. Develop diagnostics and tools to communicate results clearly to both technical and non-technical stakeholders.
- Mentor and Collaborate: You will provide peer mentorship and guidance to junior scientists, helping to elevate team-wide technical excellence.
- Drive Practical Impact: Help define the success criteria for causal measurement at Keystone, ensuring that insights from models are translated into measurable business improvements.
This role is based in Bellevue, WA or NYC with a flexible and hybrid work environment.
Basic Qualifications
- 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.
- Experience with causal machine learning frameworks and structural causal models.
- Experience with time series methods for causal analysis.
- 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.
- Proficiency with statistical programming languages such as Python, and experience deploying causal inference algorithms in production settings.
Preferred Qualifications
- 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.
- Demonstrated ability to collaborate with cross-functional stakeholders, including finance, product, and operations, to drive adoption of causal results in strategic decision-making.
- Track record of contributing to open-source libraries, publishing in peer-reviewed journals, or presenting at scientific conferences on causal inference or applied econometrics.
- Familiarity with time series forecasting, supply chain optimization, or related domains.
Salary Range: $195,000 - $308,000, plus an annual discretionary bonus, 401k contribution, and competitive benefits package. Actual compensation within the range will depend upon the level the individual is hired at based on their skills, experience, and qualifications.
At Keystone we believe diversity matters. At every level of our firm, we seek to advance and promote diversity, foster an inclusive culture, and ensure our colleagues have a deep sense of respect and belonging. If you are interested in growing your career with colleagues from varied backgrounds and cultures, consider Keystone Strategy.
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