Sr. Data Scientist, Capacity Planning at Amazon
Seattle, WA
$159,200–$215,300from the description
Jul 17, 2026
Seattle, WA
Jul 21, 2026
What this job asks for AI summary
A data scientist role on the AWS Support Capacity Planning team, focused on building forecasting and optimization models to improve operational efficiency across a global support network. Day-to-day work involves developing machine learning and statistical models — primarily in Python or R — to predict contact volumes, identify cost-saving opportunities, and surface operational insights, in close collaboration with engineering, finance, and workforce management teams.
Senior level · 4+ years · Dallas-Fort Worth-Arlington, TX · 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 660 people in the Dallas-Fort Worth-Arlington, TX area plausibly meet what this posting asks for (data scientists). range 280–990
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 $130,559 (middle half $92,500–$149,977). 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 (9)
The posting lists two locations — Dallas, TX and Seattle, WA — with the same salary band ($159,200–$215,300/year). Dallas CBSA is used as the primary; Seattle (CBSA 42660) is an equally valid location.
The '5+ years' experience gate covers SQL, Python, R, SAS, or Matlab collectively as a single requirement; Python and R are the languages explicitly called out for day-to-day modeling work, so Python is listed as the primary with the others as alternatives.
The '4+ years of data scientist experience' is the role-level experience gate and drives the overall years minimum.
'Statistical models e.g. multinomial logistic regression' is listed under Basic Qualifications as a hard gate; captured as 'statistical modeling' since no single tool is named.
Apache Spark and Hadoop appear in the responsibilities narrative as examples of large-scale processing frameworks the role is expected to use; they are treated as required given the firm language ('leverage large-scale data processing frameworks, such as Apache Spark or Hadoop').
Data visualization tools (QuickSight, Tableau, R Shiny) and data pipeline management experience are listed under Preferred Qualifications.
'Experience as a leader and mentor on a data science team' is listed as a preferred qualification but names no specific technology, so it is omitted from the skills list per extraction rules.
The title 'Data Scientist' carries no explicit seniority level word, so the title states no level; however, the 4–5+ year experience gates and scope of independent modeling ownership support a Senior classification.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): combinatorial optimization, data pipeline management.
Read the full posting
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