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Engineering & IT · Houston, TX
Data Scientist jobs in Houston, 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.
2,133
Open roles today
$38–$94/hr
Typical pay range
$127k
Median, full-time
8
Fresh in this list
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01
Open data scientist roles
12 shown of 2,133 · sorted by freshness
Senior Data Engineer - Azure / DatabricksLocation: Houston, TX - HybridPosition Type: Full-TimeA well-established Houston-based company is seeking an experienced Senior Data Engineer to join its Data & BI team and help i…
$143k - $180k
Title: Lead Data Scientist Location: Houston, TX / Remote within approved states Job Type: Full-Time, Direct Hire Salary: $143,000 – $180,000 per year Work Setting: Remote (only to these States: Georgia, Louisiana, Oklah…
$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…
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…
$100k - $200k
absolute integrity. East West Bank gives people the confidence to reach further. Overview The Financial Crime Risk Assessment Data Scientist is responsible for supporting the development, maintenance, and enhancement of…
management, drawing clear conclusions and recommending action plans when appropriate.Collect, organize, and analyze market and business data through a structured and methodical approach, ensuring accuracy and reliability…
NAVA Software is looking for a Lead Data Engineer Details: Lead Data Engineer Location: Houston TX Monday to Thursday onsite Duration: 6-12 months Position Overview The Lead Data Engineer is responsible for defining, imp…
Arthur Lawrence is looking for a Lead Data Scientist one our clients project in Houston, TX. Please find the job description below and send us your updated resume if interested: Must-Have Skills: Strong analytical and co…
$117.3k - $179.86k
What You Can Expect We are looking for an AWS Data Engineering Advisor to build applications and accelerate delivery using Agentic AI coding. As the Data Engineering Advisor, you will report to Senior Data Architect, you…
$100 per hour
development, deployment, and lifecycle management of advanced data science, machine learning, and artificial intelligence solutions... ...teams. Provide technical guidance and mentorship to other data scientists, contrib…
3 days onsite, Houston TX Senior DB2 SQL Data Engineer We are seeking a Senior DB2 SQL Data Engineer with expert-level proficiency in SQL stored procedures and deep familiarity with the DB2 IBM iSeries system (not Z/OS,…
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What data scientists earn in Houston
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$52 | $78k–$108k |
| Mid level | $52–$71 | $108k–$147k |
| Senior | $68–$94 | $142k–$196k |
Adjusted for the Houston 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.
02
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.
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
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Staff Data Scientist
05
Head of Data Science
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