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Engineering & IT · Orlando, FL
Data Scientist jobs in Orlando, 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.
535
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 535 · sorted by freshness
$50 per hour
assess mission outcomes, operational effectiveness across complex scenarios.Develop, configure, adapt, and employ simulation tools and data sets in support of military training and exercise environments.Support live, vir…
$55 per hour
DescriptionKforce has a client in Altamonte Springs, FL that is seeking a Data/ Business Analyst - Engineering.Responsibilities:* Design, develop, and maintain scalable data pipelines supporting consumer intelligence and…
$197.6k - $264.9k
Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data — replacing manual pulls, static reports, and email-driven approvals with one…
technical documentation related to software development and maintenance activities. • Evaluate program intent, output requirements, data acquisition methods, programming techniques, and system controls to support applica…
$101k - $194k
engineer who has moved beyond building models into building agents—systems that reason, plan, and act. You have a strong foundation in data science and machine learning, and you've extended that into LLM orchestration, m…
DescriptionAt HDR, our employee-owners are fully engaged in creating a welcoming environment where each of us is valued and respected, a place where everyone is empowered to bring their authentic selves and novel ideas t…
$123k - $163k
Salary: $123,000 - 163,000 per year Requirements: Bachelors degree At least 5 years of experience in data engineering, analytics, or a related field Strong SQL skills, including hands-on development of complex queries an…
$85.39k - $116.98k
Syms Strategic Group (SSG) is seeking a talented Senior Systems Engineer (Amazon Web Services (AWS) Data Engineer) - II Location: Remote Department: Veterans Affairs (VA) Type: Full Time Min. Experience: Experienced Secu…
complementary digital assets including Fandango, Rotten Tomatoes, GolfNow and GolfPass. Job Description The Opportunity As a Data Engineer II , you will help build, maintain, and enhance the data platforms and pipelines…
creativity, embraces change, and drives connection in an ever-evolving world. Job Description The Opportunity: As a Senior Data Engineer , you will help lead the design, evolution, and reliability of the data platform su…
advanced statistical techniques, including regression analysis, decision trees, predictive modeling, and machine learning. Translate data and quantitative analysis into actionable recommendations for Business Development…
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What data scientists earn in Orlando
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 Orlando 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.
05
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.
03
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
04
Staff Data Scientist
05
Head of Data Science
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