Data Scientist at Amazon
Seattle, WA
$136,000–$212,800from the description
Jul 9, 2026
Seattle, WA
Jul 21, 2026
What this job asks for AI summary
A data science role embedded in the Monetization team, focused on creator-facing products such as subscriptions, virtual currency, and gifting. Day-to-day work spans designing and analyzing A/B experiments, applying causal inference techniques where controlled testing isn't feasible, and building pricing and segmentation analyses. The role suits a quantitatively rigorous candidate comfortable with SQL, Python or R, and translating ambiguous business problems into measurable outcomes.
Mid level · 3+ years · San Francisco-Oakland-Berkeley, CA · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 570 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (data scientists). range 300–730
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $173,851 (middle half $133,298–$217,653). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
The role accepts a PhD in Economics, Statistics, Computer Science, or a related quantitative field as a substitute for 3+ years of experience — no degree is strictly required in all cases, so the degree requirement is set to None.
Compensation ranges vary by location: Seattle, WA ($136,000–$184,000), New York, NY ($153,400–$207,500), and San Francisco, CA ($157,300–$212,800). The San Francisco HQ figures are used as the primary range; the Seattle floor is the reported minimum.
Python and R are listed as interchangeable ('Python or R'); Python is named first and is the more common choice in this context.
Airflow and SageMaker appear together under 'Bonus Points' as examples of ML production tooling; they are listed as alternatives to each other since the posting treats them as a group, though in practice they serve different functions.
Double ML and causal forests appear under 'Bonus Points' as examples of modern causal ML methods and are treated as preferred, interchangeable examples of that broader skill.
The role is not fully remote — the posting explicitly states work must be done from San Francisco, CA; New York, NY; or Seattle, WA.
The alt SOC 15-2031 (Operations Research Analysts) is noted because the role has a strong economics/causal-inference and pricing-optimization flavor, but the primary framing as 'data scientist' with Python/ML methods makes 15-2051 the better fit.
Read the full posting
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