critical infrastructure that supports the digital age and shapes the spaces where people work, connect, and thrive. From high-performance data centers driving the future of AI to dynamic commercial environments, your wor…
Engineering & IT · Seattle, WA
Data Scientist jobs in Seattle, WA
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
963
Open roles today
$48–$120/hr
Typical pay range
$162k
Median, full-time
12
Fresh in this list
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01
Open data scientist roles
12 shown of 963 · sorted by freshness
$101.3k - $160k
Business Data Technologies (BDT) makes it easier for teams across Amazon to produce, store, catalog, secure, move, and analyze data... ...deliver high quality data sets to support business analyst, data scientists, and c…
$213.76k - $280.55k
particular, that includes creating an equitable, inclusive and growth-focused environment for our people.about this teamThe Enterprise Data & AI team is a strategic and operational driver of growth for lululemon, owning…
$103.28k - $191.25k
Req ID:388969NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.NTT DATA…
$89.78k - $112.08k
you to bring your civil engineering and land development experience into play as you support the planning, permitting, and design of data center and other large-scale industrial sites. This role will provide hands-on tec…
$132.1k - $178.8k
Prime Video's Partner and Content Foundations (PCF) product and data organization obsesses over partners by delivering world-class... ...AWS services. We work closely with PMs, TPMs, SDEs, and Applied Scientists, and col…
$114.6k - $234.6k
GoldenGate Service is a new multi-tenant, cloud native service for real-time data integration and replication in heterogeneous IT environments. GoldenGate enables users to replicate and integrate data from different sour…
$110k - $220k
highly scalable systems. We interact with multiple teams within the company to develop scalable, robust technical solutions. The data scientist role will be part of the data science team and will play a key role working…
$197.6k - $264.9k
Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data — replacing manual pulls, static reports, and email-driven approvals with one…
external consumers of LTPF forecasts to ensure they have timely data in the right context to answer key business questions. The... ...well-architected, and easy for others to maintain- Partner with scientists, engineers,…
management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Position: Data Engineer II Location: Bellevue, WA 98004 Duration: 6 Months Job Type: Temporary As…
$222.5k - $257.5k
rigor, reliability, and product standards as customer-facing software. As a Principal Software Engineer (Observability & Telemetry Data) , you will set the technical direction for how Smartsheet collects, models, stores,…
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What data scientists earn in Seattle
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $48–$66 | $100k–$138k |
| Mid level | $66–$90 | $138k–$188k |
| Senior | $87–$120 | $181k–$250k |
Adjusted for the Seattle market from national ranges.
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What employers ask for
The skills these listings keep naming
Python (pandas, scikit-learn)SQLStatistics and A/B testingMachine learning fundamentalsData visualizationExperiment designCommunicating with stakeholdersDomain and product sense
04
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through a data project that changed a decision.
Structure it as question, approach, finding, action. Projects that ended in a decision — even 'we did not launch' — beat technically impressive analyses that went nowhere.
How would you design an A/B test for a new feature?
Cover the metric, randomization unit, sample size, and duration, plus a pitfall like peeking or interference. Practical rigor is what is being tested.
Your model performs well offline but poorly in production. Why might that be?
Discuss train/serve skew, data leakage, distribution shift, and feedback loops. Listing several plausible causes and how you would check each is the strong answer.
Explain p-values or confidence intervals to a non-technical stakeholder.
Use plain language and a concrete scenario, and resist overstating certainty. They are testing whether your statistics survive translation.
How do you handle missing or messy data?
First ask why it is missing — the mechanism matters more than the imputation method. Then discuss options and how the choice affects conclusions.
When would you not use machine learning for a problem?
When a rule, a query, or a simple heuristic wins on cost and interpretability. Knowing when ML is overkill signals maturity.
How do you decide which metric a team should optimize?
Talk about proxy versus true goals, gameability, and counter-metrics. A story about a metric that backfired is very effective here.
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Resume tips that move the needle
For data scientists specifically — generic advice costs you here
01
Lead every bullet with the business result — revenue, retention, cost — and put the method second.
02
Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
03
Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
04
Keep one or two public projects or publications linkable, tailored to the industry you are targeting.
05
Name the size and kind of data you worked with — event streams, tabular, text — so teams can map you to their problems.
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Where this role goes
Typical progression
01
Junior Data Scientist
02
Data Scientist
03
Senior Data Scientist
04
Staff Data Scientist
05
Head of Data Science
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