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 · Austin, TX
Data Scientist jobs in Austin, 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.
1,318
Open roles today
$40–$101/hr
Typical pay range
$136k
Median, full-time
12
Fresh in this list
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01
Open data scientist roles
12 shown of 1,318 · sorted by freshness
their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.Atlassian’s Trusted Data Platform (TDP) provides secure, reliable, and scalable data-storage capabilities…
$125k - $191.5k
Technical Center three times per week, at minimum [or other frequency dictated by the business if more than 3 days].The RoleVehicle Data Engineering is looking for a Senior Data Engineer to design, build, and operate dat…
implementing database schemas, including tables, indexes, views, stored procedures, and triggers to meet business requirements and ensure data integrity.Data Modeling: Creating data models that represent the structure of…
$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…
opportunity to impact your career and provide an adventure where you can push the limits of what's possible.As a Lead Software Engineer - Data and Payments Data Platform at JPMorgan Chase within the Commercial and Invest…
community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.About the Role:CrowdStrike's Data Platform is the foundation beneath Falcon and Next-Gen SIEM: the ingestion, st…
$132.4k - $238.4k
impact?The Structural Engineer is a core member of the Facility Enablement team, responsible for the structural integrity of both the data center products we build and the manufacturing facilities used to produce them. T…
$134k - $180k
Req ID:388878NTT 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 cu…
-to-end business intelligence solutions, including interactive dashboards and reporting tools.Develop and maintain robust, scalable data models and automated Extract, Transform, and Load (ETL) pipelines ensuring high-per…
$80.46k - $142.72k
deliver more impact together.Role description:As an Electrical Engineer you will lead the electrical discipline of multiple concurrent data center projects through pursuit, proposal, design, and construction phases. You…
Preferred Work Location - Austin, TX / Secondary Work Location - Southlake, TX This role will support analytics, reporting, automation, and data- driven decision support for technology capacity planning working closely w…
02
What data scientists earn in Austin
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $40–$56 | $84k–$116k |
| Mid level | $56–$76 | $116k–$158k |
| Senior | $73–$101 | $152k–$210k |
Adjusted for the Austin 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.
04
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
04
Staff Data Scientist
05
Head of Data Science
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