~# Lead Data Engineers – Azure/ Databricks/ SnowflakeUnited States · Full-time · Senior#### About The Position**Lead Data Engineers... ...Database, and Azure Cosmos DB or Vector DB · Collaborate with data scientists, ana…
Engineering & IT · Miami, FL
Data Scientist jobs in Miami, FL
Data scientists turn raw data into decisions and products, using statistics, experimentation, and machine learning to answer questions the business could not otherwise settle.
1,298
Open roles today
$42–$106/hr
Typical pay range
$143k
Median, full-time
3
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01
Open data scientist roles
12 shown of 1,298 · sorted by freshness
$149k - $170k
RUM Group Inc. is an AI infrastructure and video company. Its Quake AI business delivers AI compute as a service, operating AI data centers including GPU and CPU compute, storage, and networking at scale. Rumble, RUM Gro…
$150k - $180k
job summary: Are you a senior data architect looking to lead a massive data modernization effort at a premier legal services provider? We are seeking a heavyweight backend engineering professional to champion the transit…
software industry, is looking for a talented and experienced Data Platform Engineer to join their innovative team. At Worth AI,... ...trustworthy, well-documented datasets Work closely with data scientists, analysts, and…
civilian workforce. This is part of the DoD SkillBridge Program, offering transitioning service members hands-on experience in Equinix’s data center operations. Interns will support routine and semi-routine tasks related…
$140k - $231k
set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Lead Data Engineer Overview We are seeking a Lead Data Engineer with expertise in Apache Spark…
our guests. The Royal Caribbean Group’s Revenue Planning & Analysis Team has an exciting career opportunity for a full time Data Analytics Engineer reporting to the Data Analytics Engineer Lead. The position is onsite an…
and ACA plans, ACOs, health systems, MSOs, and primary care groups. Our platform integrates EHR, claims, device, and care management data to power risk stratification, cohort building, care gap identification, clinical w…
Description Description: We are building the next generation of data- driven aviation software to transform aircraft maintenance and... ...operations. We are looking for an experienced Data Scientist to lead the developm…
Data Engineer Location: Miami, FL Reports to: Director of Data Department: Engineering About eMed eMed is a pioneering... ...Develop analytical tools and programs Collaborate with data scientists and architects on severa…
signals, and live portfolios rather than purely theoretical research. You will work directly with production strategies, proprietary data, and live trading systems to determine which signals are selected, how risk is man…
Must be a US Citizen or Hold a Green Card ABOUT THE ROLE We are looking to hire a Data Scientist who can transform the complexity of global shipping and logistics into clear, actionable intelligence. You'll work across o…
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What data scientists earn in Miami
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $42–$58 | $88k–$121k |
| Mid level | $58–$79 | $121k–$165k |
| Senior | $77–$106 | $160k–$220k |
Adjusted for the Miami 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.
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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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