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…
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.
957
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$42–$106/hr
Typical pay range
$143k
Median, full-time
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Open data scientist roles
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leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global de…
$122k - $145k
acceptance criteria* Create process maps that demonstrate a client’s business workflows to assist in stakeholder alignment* Capture data, reporting, security, and user experience needs at a functional level* Validate req…
DescriptionJob SummaryThe Supply Chain Systems & Data Governance Analyst plays a vital role in maintaining and optimizing the item master data that powers critical supply chain operations across the health system. Direct…
Seeking an experienced Oracle OBIEE / OAS Data Engineer with strong expertise in Oracle Analytics Server, Oracle Database, SQL/PL/SQL, ETL, and Data Warehousing. The ideal candidate will be responsible for developing and…
Analytics Engineer Location: On-Site Miami · Reports to: Director, Data & Analytics · Department: Data & Analytics About eMed eMed is a leading digital health company specializing in cardio metabolic health through manag…
and develop a talented workforce to create and deliver a wide range of content reflecting our world. Job Description The Data Engineering Manager will be a hands-on data engineering role supporting Universal+, Hayu, and…
Zemsania busca Data Engineer Acerca de Zemsania: En Zemsania, somos líderes en la transformación digital, impulsando el éxito de nuestros clientes a través de soluciones tecnológicas innovadoras y de vanguardia. Contamos…
innovadoras y personalizadas en el ámbito de las TI, cubriendo desde el desarrollo de software hasta la implementación de estrategias de Big Data e Inteligencia Artificial. Nuestro equipo está formado por profesionales a…
We are seeking an experienced Azure SQL Database Administrator (Azure SQL DBA) to manage, optimize, and support enterprise database environments. The role requires strong expertise in Azure SQL Database, Microsoft SQL Se…
the cutting edge of technology, energy, and infrastructure. Hut 8 is on a mission to build and operate some of the world’s largest data centers for next-generation computing workloads, including AI, Colocation, Cloud, an…
values technical excellence, ownership, proactive communication, and collaboration. About the Role We are looking for a Senior Data Platform Engineer to join a high-impact engagement with one of our partners, a global le…
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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
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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
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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
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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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