$85k - $116.8k
Manufacturing Group, our team elevates our clients by delivering cost-effective and transformative projects to the electric vehicle, data center, consumer goods and products, metals, pulp and paper and various other s. W…
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.
580
Open roles today
$42–$106/hr
Typical pay range
$143k
Median, full-time
1
Fresh in this list
No email, no resume, no sign-up. Save any listing below and you start anonymously.
You're signed in. Saving a listing drops it straight into your pipeline.
01
Open data scientist roles
6 shown of 580 · sorted by freshness
required for this role ~ AWS, Databricks certifications nice to have, not required ~8-10 years' experience minimum preferred in a data- related role ~ Prior Nike experience required ~ Databricks Spark Python Snowflake SQ…
Work options: Hybrid Mon-Thurs onsite. Typical Title: Data Engineer | Machine Learning Engineer Locations : Beaverton, OR... ...across the pipelines you own. Collaborate with data analysts, scientists and product teams t…
$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…
playmakers, risk takers and glue players. Are you game? LEAD, DATA ENGINEER - NIKE [Beaverton, OR - USA] WHO YOU'LL WORK... ...Reporting to the Engineering Director, this team partners with data scientists, engineers, an…
organisations design, deliver and support complex IT infrastructure. Their expertise spans enterprise networking, cloud, cybersecurity, data centre technologies and managed services, delivering tailored solutions that ke…
02
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.
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.
06
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
Ten quiet minutes a day
Applying for data scientist jobs in Portland?
Robbi carries this page into your first day: your role, your city, your shift preference. Then it hands you a few small things each morning and keeps the pipeline honest.
Save what looks right here, then let Robbi hand you a few small things each morning and keep the follow-ups honest.
Start with this search
No email, no resume, no sign-up.
Open your tracker
Everything you saved is already there.