Skills: Snowflake and Data pipelines Python AI/ML and Advanced Analytics Building and deployment AI agents... ...candidate will work closely with the Operations team, as well as data scientists and software engineers, on…
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.
2,259
Open roles today
$40–$101/hr
Typical pay range
$136k
Median, full-time
3
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01
Open data scientist roles
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Hi , Data Engineer Location Austin, TX Skills: Proven hands-on experience in Snowflake database . Must have - Proven hands-on experience in working on solutions using Apache Doris. Proficient in data modeling and writing…
Description Who We Are Overhaul is a supply chain integrity solutions company that allows shippers to connect disparate sources of data into the first fully transparent situational analysis engine designed for the logist…
Senior Databricks Data Engineer (Azure Databricks | Lakehouse | ETL/ELT) Austin, Texas (Hybrid – 3 Days Remote | Onsite Every Monday & Thursday) Experience: Minimum 12+ Years Mandatory Requirements ✔ Databricks Certified…
$37 per hour
Mindrift is looking for highly skilled Python Data Scraping Engineers to join the Tendem project and drive specialized data scraping workflows within our hybrid AI + human system. In this role, as an AI Pilot – that’s ho…
, and collaborative environment. Who We Want As a Senior Data Engineer at Arrive Logistics, you will build and own the data ecosystem... ...batch machine learning pipelines. Work closely with data scientists to orchestra…
winning culture that supports personal and career development in a fun, casual, and collaborative environment. Who We Want The Data Scientist II will work closely with Data Science, Product, and Engineering to build and…
Job Description Job Description Position Summary: MedReview is looking for a talented and experienced Data Scientist to join our dynamic team. As a part of our team, you will leverage your analytical skills and expertise…
where they will be challenged to improve results through real-life engineering and data analysis and where everyone’s implication has an impact. We are looking for a Growth Data Scientist to work closely with internal an…
Description: ABOUT TRUDATARX TruDataRx, Inc. uses objective clinical data to help clients improve the clinical efficacy and reduce the... ...data and actionable analytics, ensuring our analysts and data scientists have c…
Job Description Job Description As an early hire to our engineering team, you will be responsible for managing Loxo’s data integration function. You will be primarily responsible for migrating new clients’ legacy data fr…
met with unmatched rewards as we transform the hospitality and experiences industry globally. We are seeking an experienced Senior Data Engineer to establish and lead our data infrastructure as an early member of our dat…
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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
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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
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Junior Data Scientist
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Data Scientist
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Senior Data Scientist
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Staff Data Scientist
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Head of Data Science
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