Discover more by following us on LinkedIn! GENERAL FUNCTION The Data & ML Engineer is a self-sufficient engineering professional... ...and AI solutions. This role complements the Applied Data Scientist by owning the engi…
Engineering & IT · Dallas, TX
Data Scientist jobs in Dallas, 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.
1,710
Open roles today
$38–$96/hr
Typical pay range
$130k
Median, full-time
5
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01
Open data scientist roles
12 shown of 1,710 · sorted by freshness
Data Engineer Company: Gigapower LLC Dept./Org.: Trans./Strategy Location: Virtual (Remote) Who We Are Gigapower is building next-generation fiber broadband infrastructure that expands high-speed connectivity to communit…
Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies…
Job Role: Data Engineer Location: Dallas, TX Duration: Long Term project Full Time Position Job Description: Data Engineer skilled in building scalable cloud data pipelines for high-volume manufacturing and IoT data usin…
Job Title: Senior Data Engineer Location: Dallas, TX Job Description: hands-on experience in Data Engineering. Strong expertise in Snowflake, Qlik Replicate, DBT Cloud, Astronomer Airflow, Python, and PySpark. Experience…
Job Title : Data Engineer Location : Dallas, TX Job Description: Bachelor's degree or Master's Degree in Computer science, or a related field, with minimum 10+ Years of relevant experience Strong Python development skill…
Role Description ~12+ years of experience as a Data Engineer. ~ Design, develop, and maintain scalable, resilient data engineering solutions. ~ Strong expertise in Snowflake, Python, PySpark, DBT, Qlik Replicate, and Air…
$69.2k - $153.3k
Data Engineer Position Description CGI is seeking to hire a Data Engineers passionate about building scalable data solutions and enabling data driven decision making! Join our growing engineering team and help design, de…
$157.41k - $207.8k
Information Job Code: 00194008 Job Family: Research and Development Job Function: Software Engineering About the Job Senior Data Engineer Location: Irving, Texas Job Duties: Avaya LLC is seeking a Senior Data Engineer (I…
$120k - $140k
Title: Senior Data Engineer Location: Coppell, TX Salary Range: $120K-$140K We are looking to bring on a Senior Data Engineer to our team due to growth. What we need is someone who comes 8+ years of experience in the Dat…
Senior Data Engineer At Billee , we’re building the next generation of utility billing. Our goal is simple: make a complex, manual, and fragmented process feel seamless, transparent, and intelligent. Our intelligence pla…
following job description: We are seeking a highly skilled Data Engineer to design, build, and optimize enterprise-grade data solutions... ...principles. Collaborate with cross-functional teams (data scientists, analysts…
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What data scientists earn in Dallas
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$110k |
| Mid level | $53–$72 | $110k–$150k |
| Senior | $70–$96 | $145k–$200k |
National ranges — pay in Dallas typically tracks these.
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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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