$71.3k - $140.6k
clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation. Work you'll do As a PROJECT - Data Engineer II on the AI & Data team, you will be responsible for… Designing, de…
Engineering & IT · Portland, OR
Data Scientist jobs in Portland, OR
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
664
Open roles today
$42–$106/hr
Typical pay range
$143k
Median, full-time
6
Fresh in this list
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01
Open data scientist roles
11 shown of 664 · sorted by freshness
needs. This in-office requirement may be adjusted at the discretion of the company.Columbia Sportswear Company is seeking a Senior Data Engineer in Portland, OR responsible for designing and implementing Kimball-style di…
$122k - $145k
acceptance criteria* Create process maps that demonstrate a client’s business workflows to assist in stakeholder alignment* Capture data, reporting, security, and user experience needs at a functional level* Validate req…
$114k - $142k
SSO customers.Build APIs and internal tooling that let other engineering teams and internal stakeholders manage and troubleshoot user data and access controls.Collaborate closely with product management, product design,…
Stantec’s Buildings team is on a mission to become the world’s leading integrated design practice. Our architects, engineers, interior designers, consultants, sustainability specialists, and technologists are passionate…
$78k - $105k
Streaming Data Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic o…
Job Title: Sr. Data Engineer Location: Portland, OR Onsite Job Type: Contract Experience: 14+ Years Mandatory Interested candidates are required to include their LinkedIn URL, email address, and cell phone number along w…
$78.91k - $110.27k
diverse experiences and perspectives help us become a stronger organization. Let’s be better together. Position Summary The Data Engineer role on the Data Science Team (DST) is responsible for designing, maintaining, and…
evolving game. NIKE is a technology company. From our flagship website and five-star mobile apps to developing products, managing big data, and providing leading edge engineering and systems support, our teams at NIKE Gl…
and passion to a challenging and constantly evolving game. Data science is a competitive differentiator for Nike and is fundamentally... ...LOOKING FOR We are looking for an experienced Senior Data Scientist to join our…
About Us Swan Island Networks has been developing situational intelligence and alerting software for over a decade. Swan Island's current TX360® platform helps companies make faster, better informed decisions in mission…
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What data scientists earn in Portland
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $42–$58 | $88k–$121k |
| Mid level | $58–$79 | $121k–$165k |
| Senior | $77–$106 | $160k–$220k |
Adjusted for the Portland 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.
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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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