$69k - $109k
automation solutions within our internal applications. We develop API-driven integrations with secure authentication and dependable data transfer. We use Microsoft development tools, including Visual Studio and Azure-bas…
Engineering & IT · Albuquerque, NM
Data Scientist jobs in Albuquerque, NM
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
215
Open roles today
$36–$89/hr
Typical pay range
$121k
Median, full-time
6
Fresh in this list
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01
Open data scientist roles
12 shown of 215 · sorted by freshness
$70k - $105k
your expertise to solve challenges and celebrate success! Job Summary JCS Solutions LLC is seeking a highly skilled Senior Data/ Visualization Engineer specializing in data visualization to join our team. The primary foc…
$62k - $85k
Careerscape is recruiting on behalf of our client, a growing organization seeking a motivated Remote Help Desk Technician to join its IT support team. This is a great entry point into a technology career, offering hands-…
Desktop (AVD) Experience managing enterprise endpoint security, device compliance, application deployment, endpoint configuration, data governance, information protection, and Data Loss Prevention (DLP). Experience with…
Grow, innovate, and generate progress: Harness your expertise to solve challenges and celebrate success! Job Summary JCS Solutions is seeking a highly skilled Multi-Cloud Engineer SME to support mission-critical Departme…
$103k - $155k
connect, Zero Trust Network Access, Role- and Attribute- Based Access Controls (RBAC/ABAC), Working knowledge of Purview or similar data governance platforms. Experience implementing Infrastructure as Code and cloud auto…
$103k - $155k
provide real-time situational awareness. Infrastructure Telemetry Integration: Normalize and correlate network performance and fault data from SolarWinds with server and application logs to provide a holistic view of ent…
$65k - $136.5k
science and technology communities to support future strategy and capability gap identification. Your role will involve assisting in data collection, document development, technical writing, editing, and graphics creatio…
Director of Human Resources Lubricar, Inc. Vision and Mission Vision Statement: We Are Driven To Make Your Life Easier Mission Statement: Lubricar is the Leading Provider of Quality Convenient Automotive Maintenance and…
We are seeking an experienced ServiceNow IRM Architect to lead the design, implementation, and optimization of enterprise Integrated Risk Management solutions on the ServiceNow platform. The role requires deep expertise…
$116k - $210k
product realization processes, technical reviews, and interagency coordination meetings. Lead or support preparation and response to data calls, Congressional inquiries, and short-notice requests from NNSA senior leaders…
should have experience developing and implementing machine learning algorithms with a focus on some combination of generative methods, data fusion, classification, verification and validation, optimization, computer visi…
02
What data scientists earn in Albuquerque
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $36–$49 | $74k–$102k |
| Mid level | $49–$67 | $102k–$140k |
| Senior | $65–$89 | $135k–$186k |
Adjusted for the Albuquerque 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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