architecture| Service Fulfilment (Provisioning and Activation) OSS/BSS integration| CMDB| network inventoryEvent-driven and real-time data architectures (Kafka| REST APIs) Essential Skills: Technical: SPARQL| RDF| OWL| s…
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.
2,151
Open roles today
$42–$106/hr
Typical pay range
$143k
Median, full-time
2
Fresh in this list
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01
Open data scientist roles
12 shown of 2,151 · sorted by freshness
skilled professional with strong expertise in SQL, Python, Spark, and leading AI/ML frameworks to design and build scalable, intelligent data solutions. The ideal candidate will have hands-on experience with big data pla…
Lead Data Scientist Location - Denver , CO Your Role We are seeking a highly experienced Senior AI Architect / Lead Engineer to lead the design, architecture, and development of next-generation AI-powered Customer Experi…
We are looking for an experienced Data Engineer Consultant to join our team. The ideal candidate will possess strong SQL development... ...improvements. Work closely with client teams, data scientists, analysts, and othe…
$100.98k - $136.63k
include a live whiteboarding or technical assessment session. You will address the challenge of transforming complex, fragmented data requirements into scalable, production-ready AI infrastructure using advanced Databric…
$125k - $170k
Position: Data Engineer Location: Centennial, CO Clearance: TS/SCI Required Grey Matters Defense Solutions stands at... ...pipeline. The pipeline end-to-end goes from our Analyst to our Data Scientist on the program. Thi…
caliber job description focused on building a world-class engineering organization. Fluent Conveyors is building one of the most data- driven conveyor manufacturing organizations in North America. Every conveyor, compone…
$160k - $200k
Description Description TL;DR Kharon is seeking a full-time Senior Data Engineer based in Denver, Colorado. This role requires in-... ...Data Lake. Collaborate with engineers, architects, and data scientists to implement…
$90k - $107k
Job Description Job Description SUMMARY: The Data Engineer at Hercules Industries is responsible for building, governing, and continuously... ...Build data models that make data accessible for analysts, data scientists,…
$40 - $60 per hour
We are looking for a talented Data Developer to join one of our top communications partners. This person will provide additional support for several new Emerging Tech-related products that they collect data on. The ideal…
$40 - $60 per hour
Location: Onsite in Denver, CO W2 only Senior Data Engineer Requirements: ~8+ years experience with BI tools (Power BI, Tableau, Amazon QuickSight). ~8+ years in data engineering (ETL, data transformation, automation). ~…
At Candid Health, we’re searching for our first BI (Analytics) Engineer to bridge Analytics Engineering, Business Intelligence, and Data Analysis. As a key strategic investment for the company and product, you will be re…
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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.
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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.
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Specify your stack concretely: Python libraries, SQL dialects, experimentation platforms, and any production ML experience.
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Distinguish shipped work from research; 'model serving 2M users' and 'notebook analysis' are different claims.
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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
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Staff Data Scientist
05
Head of Data Science
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