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 · Denver, CO
Data Scientist jobs in Denver, CO
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
1,293
Open roles today
$42–$106/hr
Typical pay range
$143k
Median, full-time
11
Fresh in this list
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01
Open data scientist roles
12 shown of 1,293 · sorted by freshness
$89.8k - $229.3k
and marketing services driven by aggregated and de-identified data insights and award-winning creative services. Spectrum Reach helps... ...about Spectrum Reach can be found at the Lead Data Scientist, you are responsibl…
$90k - $120k
Exposure to GCP, BigQuery, and Looker Familiarity with GitLab or similar source control tools Responsibilities: Lead regulatory data projects from requirements gathering through implementation Build, enhance, and support…
$132.26k - $155.6k
career. Try new things, learn new skills and discover what you excel at—all from Day One.Job DescriptionJob Duties -Responsible for big data/ analytics projects that gather and integrate large volumes of data. -Specializ…
$65 - $75 per hour
DescriptionKforce has a client that is seeking a Data Infrastructure Engineer in Greenwood Village, CO.Summary:We're looking for a Data Infrastructure Engineer to build and optimize scalable data pipelines that support d…
$159.2k - $215.3k
Application deadline: Oct 3, 2026Are you excited about using data to shape decisions that reach millions of customers? Do you thrive... ...impact problems with creative analytical approaches? As a Data Scientist III at A…
$110.8k - $196.4k
this Principal Engineer position leads the architecture, engineering, administration, and strategic evolution of our Hadoop-based big data platform. This role is responsible for defining the technical direction of the en…
Company DescriptionOverviewEpsilon’s Data Science & AI practice within Analytics Services team is seeking a Senior Staff Data Scientist to lead the delivery of advanced analytics and AI solutions that drive measurable bu…
$82.1k - $172.4k
sensing, including Automatic Target Recognition (ATR) and Multimodal Data Fusion for Electro-Optical (EO) and Synthetic Aperture RADAR (... ..., age, national origin, disability, status as a protected veteran, or any oth…
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…
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…
$120k - $155k
Location: Lakewood, CO, Onsite Reports to: Director of Data & Systems Infrastructure About the Role Bloom Healthcare is looking for a Data Engineer to build, manage, and maintain the pipelines and data infrastructure tha…
02
What data scientists earn in Denver
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 Denver 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.
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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