We are seeking a Snowflake Data Analyst/Engineer with strong expertise in Snowflake, SQL, and data pipeline validation. The ideal candidate will be responsible for designing and optimizing data models, validating data pi…
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.
1,714
Open roles today
$48–$120/hr
Typical pay range
$162k
Median, full-time
4
Fresh in this list
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01
Open data scientist roles
12 shown of 1,714 · sorted by freshness
$80 - $85 per hour
Job Description Job Description Data Engineer Location: Seattle, WA Onsite Flexibility: Hybrid Contract Details Position... ...is accessible and reliable. Collaborate with analysts and scientists to provide clean, struct…
$122.9k
Job Description Job Description Audience Data Scientist (Marketing Activation) We are hiring an Audience Data Scientist to identify high-value customer and prospect opportunities and translate them into analytically defi…
performance benchmarks that guide the development of all AI systems at the company. You will work closely with machine learning and data engineering teams to evaluate model performance, identify failure modes, and guide…
$90 - $125 per hour
Job Description Job Description LHH Recruitment Solutions is seeking a Senior Data Analyst for a remote contract opportunity supporting a growing healthcare analytics and consulting organization. This role will partner w…
$121.6k - $243.2k
Technology Machine Learning Engineer Graduate (TikTok- Data- Search-Local Service) - 2027 Start Location : Seattle Employment Type : Regular Job Code : A259310 Responsibilities Our Search Engineering Team is responsible…
Technology Machine Learning Engineer Graduate ( Data- Global E-Commerce-Search) - 2027 Start Location : Seattle Employment Type : Regular Job Code : A126983 Responsibilities The Search E-Commerce team spearheads the deve…
$142.3k - $263.3k
people globally? Within the evaluation organization, the mission of Data Science and Insights team is to guide product development,... ...technical topics (especially causal topics) to everyone from data scientists to en…
$140k - $185k
We are supporting our client on a search for a Senior Data Engineer who will be the technical owner of a modern data platform. This is a high-trust seat reporting to the head of data with real budget, real ownership, and…
Job Description Job Description We are looking for a Senior Data Engineer to join our growing data platform team. You will own the design, build, and reliability of our cloud-native data lakehouse — from raw ingestion th…
Job Description Job Description Job Summary We are seeking a highly skilled and motivated Data Engineer to join our growing data team. The Data Engineer will be responsible for designing, building, and maintaining robust…
$155.7k - $208.7k
Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for…
02
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.
03
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.
05
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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