We are seeking a Data Engineer with strong Databricks expertise to modernize and scale our Business Intelligence (BI) capabilities. This role will design and build data pipelines, deploy machine learning solutions, and o…
Engineering & IT · Tampa, FL
Data Scientist jobs in Tampa, 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.
655
Open roles today
$38–$95/hr
Typical pay range
$129k
Median, full-time
7
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01
Open data scientist roles
12 shown of 655 · sorted by freshness
$92k - $130k
the rental residential real estate business. To learn more, visit . JOB DESCRIPTION SUMMARY Greystar's D²AI organization ( Data, Digital, and AI) is responsible for the platforms, processes, and practices that power anal…
Job Description As Data Scientist Lead within Commercial & Investment Bank with the Healthcare Provider team, you will lead a team in building advanced solutions for image classification, text categorization, and intelli…
review of the types of work performed Other jobrelated duties may be assigned as required Indepth understanding of HDFS architecture data storage and fault tolerance mechanisms Experience with HDFS commands and administr…
smarter, faster, and more securely in dynamic environments. JOB DESCRIPTION MEANINGFUL WORK AND PERSONAL IMPACT The Data Scientist- Mid will support Defense & Intelligence Programs at U.S. Special Operations Command (USS…
Black Cape Title: Python Data Engineer (Entry - Mid Level) Location: Tampa, FL Onsite: Expected to go onsite (into a SCIF) up to 5 days per week Clearance: TS/SCI (no poly required) ***If you do NOT have a US Citizenship…
Location Designation: Hybrid - 3 days per week Role Overview: As a Corporate Vice President, Data Scientist on the Advanced Analytics team you will serve as a strategic thought partner to business leaders across New York…
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…
Strong hands-on experience in Python for data engineering and application development. Extensive experience with AWS cloud services, including S3, EMR, Glue, Lambda, IAM, EC2, ECS/EKS, CloudWatch, and Redshift. Strong ex…
Title: Enterprise Analytics Data Engineer Company : Tampa Electric Company Location: Midtown East Tower State and City: Florida - Tampa Shift: 8 Hr. X 5 Days Recruiter: Mark Koener TITLE: Enterprise Analystics Data Engin…
of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. Senior Data Scientist EY is the only professional services firm with a separate business unit (“FSO”) th…
$80 - $85 per hour
Unlock your potential as a Data Engineer in a fully remote position, where you'll play a crucial role in advancing Big Data and Analytics initiatives. We are seeking a talented individual who is passionate about leveragi…
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What data scientists earn in Tampa
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$52 | $79k–$109k |
| Mid level | $52–$71 | $109k–$148k |
| Senior | $69–$95 | $144k–$198k |
Adjusted for the Tampa 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.
04
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
04
Staff Data Scientist
05
Head of Data Science
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