Top Data Science Jobs in Tampa, FL

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and drive measurable outcomes with cross-functional stakeholders. The role requires independent analytical work, strong problem-solving, persuasive communication, and ownership of ambiguous initiatives.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Own end-to-end analytical functions for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; derive insights; communicate recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires independent data work, first-principles problem solving, persuasive communication, and ownership of ambiguous initiatives.

The Data Scientist will support intelligence programs by developing data-driven solutions, predictive models, and machine learning tools. Responsibilities include analyzing complex datasets, evaluating data sources, improving model and data accuracy, developing Python and API-based solutions, and partnering with stakeholders on business outcomes. The role requires SQL and large-scale data system experience, works independently, may mentor junior professionals, and is fully onsite at MacDill AFB.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Design and deploy responsible generative AI solutions using LLMs and agentic AI frameworks. Collaborate with MLOps/DevOps, engineers, and business partners to integrate scalable, reliable models into enterprise workflows, build monitoring and ETL pipelines, and communicate results to stakeholders.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Leads data science architecture and advanced analytics for government intelligence missions. Develops scalable Python pipelines, geospatial and multi-INT analytics, machine learning models, network analysis, and interactive dashboards. Translates intelligence requirements into production solutions, mentors data scientists, researches innovative methodologies, and delivers GEOINT products aligned with NGA and DNI standards.

Develop machine learning and AI models using clinical, claims, and social determinants of health data to improve pediatric patient outcomes. Partner with stakeholders and cross-functional teams to define requirements, optimize data collection, build data assets, conduct exploratory analysis, and deliver strategic and patient-care insights. Query Snowflake independently and present findings to nontechnical staff.

Analyze complex structured and unstructured data; design data pipelines; develop predictive, prescriptive, NLP, clustering, forecasting, and machine learning models; automate data processing; use cloud and distributed computing platforms; create visualizations; collaborate with data engineers; research algorithms; and communicate insights to nontechnical audiences supporting U.S. Special Operations Command.

Leverage complex structured and unstructured data to support U.S. Special Operations Command decision-making and digital transformation. Design and maintain data pipelines, perform statistical analysis, develop predictive and prescriptive models, and implement machine learning, NLP, forecasting, clustering, and text-processing solutions. Use cloud data platforms and distributed processing frameworks to integrate, analyze, and visualize data. Collaborate with data engineers, communicate findings to nontechnical audiences, and support mission readiness initiatives.

Analyze and optimize digital partnership performance, growth, profitability, and customer acquisition economics. Design experiments, develop forecasts and performance models, build measurement frameworks, investigate anomalies, and provide actionable recommendations. The role requires translating ambiguous business questions into analytical solutions, communicating insights to technical and non-technical stakeholders, and establishing scalable analytical processes that support strategic decision-making.

The Data Scientist will design and deploy AI-enabled data solutions, developing data pipelines and collaborating with teams to transform data into insights.

Lead the strategic direction and evolution of Netflix’s company-wide Experimentation Platform. Establish rigorous causal inference and experimentation standards, automate trust and validation practices, consolidate fragmented systems, shape platform product roadmaps, and collaborate with data science and engineering leaders. Translate experimentation needs into scalable tools, influence adoption across teams, mentor colleagues, and ensure reliable experiment allocation, measurement, analysis, and reporting.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Leads advanced data analysis to guide product and business decisions, develops metrics, dashboards, data products, and AI-enabled reporting systems, and translates findings into strategic recommendations. Partners with data engineers on dimensional data models, data governance, and quality controls. Mentors analysts, aligns roadmaps with business priorities, improves problem framing, and communicates insights effectively to technical and business stakeholders.

Build and maintain production-quality Python code for portfolio construction, optimization, and statistical analysis. Automate investment workflows, validate models, and create interactive visualizations to support investment decision-making and reporting.

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead end-to-end development and deployment of predictive and hybrid deep learning/LLM models for insurance use cases. Prototype, validate, and productionize models using modern ML frameworks and MLOps, collaborate with product and engineering, and communicate technical results to stakeholders to drive measurable business impact.

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

Provide on-site data science and operations research support for a combatant command. Design and build analytics, AI/ML, and automated data-collection pipelines; create real-time/near-real-time visualizations, decision briefs, and analytic products; integrate data into C2IE, MAVEN, and Advana; lead small teams and support joint operational planning and knowledge management.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.