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Engineering & IT · San Antonio, TX
Data Scientist jobs in San Antonio, 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.
403
Open roles today
$36–$89/hr
Typical pay range
$121k
Median, full-time
8
Fresh in this list
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01
Open data scientist roles
12 shown of 403 · sorted by freshness
$71.65k - $85.82k
Position: , Assignment Level: Area ~ Technology Job Title: Data Visualization/Reporting Programmer(Student) School: Campus Description: Date Posted: 10/1/2026 Deadline: 2026-11-30 Openings...
$143.32k - $273.93k
We expect 6+ years of experience in predictive analytics or data analysis, or an advanced degree such as a masters or PhD plus 4... ...methods, technologies, and approaches. We mentor junior data scientists in modeling,…
Data scientist
Known for being a great place to work and build a career, KPMG provides audit, tax and advisory services for organizations in today's most important industries. Our growth is driven by delivering real results for our cli…
$114.08k - $218.03k
supporting complex deployments and infrastructure management, or comparable IT experience such as IT management, software development, data engineering, or agile product/release management. We require 4 years of experien…
$114.08k - $218.03k
policy and business needs.The OpportunityUSAA is looking for an experienced Decision Science Analyst Senior to join the Life Company Data and Analytics team. USAA Life Company is looking to build on its foundation of exc…
$123k - $163k
audiences Responsibilities: Design, implement, and maintain security measures to safeguard enterprise systems, applications, and data Monitor and address security incidents, alerts, and events; engage in incident respons…
$65.5k - $134k
Foundational: Experience designing, building, and maintaining production-grade LLM applications, including end-to-end pipelines from data ingestion through model output delivery (e.g. Azure OpenAI, AWS Bedrock, Google Ve…
$91.4k - $190k
system design across service boundaries, orchestration layers, data flows, security controls, and external integrations. Lead workstreams... ...managing and mentoring teams of AI engineers and data scientists through the…
Title: Senior Software and Data Engineer Belong. Connect. Grow. with KBR! KBR's National Security Solutions team provides high-end engineering and advanced technology solutions to our customers in the intelligence and na…
Microsoft 365, Azure, CompTIA Security+, ISC2, or equivalent). Experience designing and implementing information systems, conducting data modeling, internal controls analysis, and performance measurement. Desired Skills…
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What data scientists earn in San Antonio
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 San Antonio 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
04
Staff Data Scientist
05
Head of Data Science
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