Key job 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.
About the team
Amazon's People Experience and Technology Central Science (PXTCS) team uses economics, behavioral science, statistics, machine learning, applied science, and Generative AI to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science, engineering, and UX to develop and deliver solutions that measurably achieve this goal.
BASIC QUALIFICATIONS
- 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 QUALIFICATIONS
- 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
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, San Francisco - 192,200.00 - 260,000.00 USD annually
USA, VA, Arlington - 167,100.00 - 226,100.00 USD annually
USA, WA, Bellevue - 167,100.00 - 226,100.00 USD annually
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
