$138.1k - $153.5k
Job Description Job Description Data Scientist 3Location: San Antonio, Texas Clearance Required: TS/SCI with Polygraph Employment Type: Full-Time Salary Range: $138,100-$153,500Join a Growing Team at Weeghman & BriggsWee…
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.
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$36–$89/hr
Typical pay range
$121k
Median, full-time
1
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11 shown of 830 · sorted by freshness
Job Description DART ( Data, Analytics and Reporting Team) serves as a provider of data, analytics, and automation solutions for... ...of the most effective solutions. Job summary: As a Data Scientist Senior Associate in…
$45 per hour
Mindrift is looking for highly skilled Vibecode specialists to join the Tendem project ( and drive specialized data scraping workflows for real-world use cases. Mindrift is looking for highly skilled Senior Python Data S…
$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…
$23 per hour
evaluating factual accuracy, or comparing responses - when projects are available. Responsibilities Carefully review provided data (text, images, or videos) Label or classify content based on project guidelines Identify…
Job Description Job Description Overview CTG is seeking an experienced Data Engineer (DataStage / Snowflake) to support enterprise data integration and analytics initiatives. This role is ideal for a hands-on data profes…
SWBC is seeking a talented individual to join our dynamic Data team. The ideal candidate will have some experience in building and... ...and third‑party ingestion frameworks. Collaborates with data scientists, ML enginee…
$92k - $126k
Duties Employ some combination (2 or more) of the following skill areas: Foundations: (Mathematical, Computational, Statistical) Data Processing: (Data management and curation, data description and visualization, workflo…
IntelliGenesis is seeking a Senior Data Scientist to support IG Labs working to answer challenging questions and problems that our customers are facing. Our data-rich environment allows you to use your knowledge and expe…
critical environment that values continuous improvements. This position manages the installation and maintenance of mechanical systems at Data Centers and operations of specialized cooling systems. Acts as an Engineering…
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
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Staff Data Scientist
05
Head of Data Science
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