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 · Dallas, TX
Data Scientist jobs in Dallas, TX
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
1,401
Open roles today
$38–$96/hr
Typical pay range
$130k
Median, full-time
12
Fresh in this list
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01
Open data scientist roles
12 shown of 1,401 · sorted by freshness
$145k - $165k
OverviewAs a Lead of Analytics Engineering at Avison Young Technologies, you will lead the strategy and execution of our proprietary data products and models that empower commercial real estate decision makers across inv…
Contract-to-HireCategory: Information TechnologyReference ID: 10061445Job record: a1qPL000005ltm9YAAValid through: 2026-11-11Title: Senior Data EngineerLocation: Coppell, TXSalary Range: $120K-$140KWe are looking to brin…
$132.1k - $178.8k
Are you passionate about standardizing data platforms and automating data engineering to drive analytics and reporting? Do you excel in dynamic, fast-paced environments and find joy in converting data into actionable ins…
$87.7k - $139.7k
experienced Senior High Voltage Electrical Engineer to lead the design and engineering of critical electrical infrastructure for hyperscale data centers and mission-critical facilities. This role provides technical leade…
$111.8k - $154.8k
certifications required to access markets around the world. Our team plays a leading role in understanding customer needs for security, privacy, data protection, and customer data management. We inform, support, and coll…
Job-ID29314469Reference26-26222In this contingent resource assignment, you may: Consult on or participate in moderately complex initiatives and deliverables within Software Engineering and contribute to large-scale plann…
Managing Director, Data Engineering & AIWho You'll Work WithAs a Managing Director in Slalom's Data & AI practice, you will lead the growth of our Data Engineering & AI business by helping clients modernize their data fo…
$40 - $45 per hour
Data Center Field Engineer Dell PowerEdge Servers (Travel Team)OverviewJoin a high-impact team supporting some of the most advanced AI and enterprise computing environments in North America. We are seeking experienced Da…
operations worldwide. For more information, visit . Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description:The Staff Data & Analytics Engineer, Domain Enablement is a senior, hands-on role responsible f…
$96.49k - $144.74k
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.We are currently seeki…
and refine threat models, perform security reviews, and define security requirements for network devices products entering Google’s data centers focusing on verified boot and embedded components.Perform AI-supported secu…
02
What data scientists earn in Dallas
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$110k |
| Mid level | $53–$72 | $110k–$150k |
| Senior | $70–$96 | $145k–$200k |
National ranges — pay in Dallas typically tracks these.
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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