About the job
As a Senior Applied Scientist on this team, you will be the connective tissue between innovative research and real-world impact. You will work shoulder-to-shoulder with economists who deeply understand the causal mechanisms driving workforce dynamics and data scientists who know the operational landscape — and you will bring the technical creativity to expand what's possible. That means writing production-quality code that our partner engineering teams can implement into decision-making tools. It means exploring novel feature spaces — large language models, computer vision, and other emerging techniques — to unlock signal that traditional approaches miss. And it means doing all of this with the scientific rigor that causal claims demand.
Responsibilities
Design and build causal predictive models that move beyond correlation — developing systems that forecast workforce outcomes and identify the actionable drivers behind them, enabling leaders to intervene before problems materialize
Pioneer novel feature engineering by bringing creative approaches from LLMs, computer vision, and other emerging techniques into the causal modeling pipeline, unlocking signal that traditional econometric and tabular methods miss
Write production-quality science code that your partner engineering team can implement directly into operational decision-making tools — your work must be clean, well-documented, and built to scale
Bridge disciplines by translating between economists, data scientists, and engineers — synthesizing causal rigor with ML innovation to produce models that are both scientifically defensible and operationally useful
Design and execute experiments to validate causal claims and model performance, establishing evaluation standards that the team and stakeholders trust
Develop and elevate peers across the team — mentoring scientists in adjacent disciplines, sharing technical knowledge, and raising the collective bar on modeling and engineering practices
Present findings to senior leadership, distilling complex causal and predictive insights into clear recommendations that drive workforce strategy for Amazon's Tier 1 hourly populations.
Qualifications
Minimum
PhD, or Master's degree and 6+ years of applied research experience
5+ years of building machine learning models for business application experience
Experience programming in Java, C++, Python or related language
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
Preferred
PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
Experience with neural deep learning methods and machine learning
Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science workflows
Experience in taking a product from conception & definition phase through engineering design and taking it to market
Experience working with emerging technologies
Experience in mentoring, leading and coaching