Top Data Science Jobs in Tampa, FL

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Lead advanced analytics and ML efforts to extract insights from large-scale historical data, design features and data representations for models and LLM/agentic systems, partner with product and engineering to build data pipelines and prototypes, evaluate AI workflows, and drive innovation through experiments and empirical analysis to inform product strategy.

This role involves leading data science projects, developing machine learning models, collaborating with stakeholders, and mentoring other data scientists.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Lead a team to design, build, and deploy multimodal document understanding solutions (image classification, text categorization, OCR extraction). Own ML lifecycle from prototyping to production on AWS, drive experimentation, data quality, annotation, and cross-team integration with engineers.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

The Data Scientist supports AI-driven initiatives, conducts analytics, builds machine learning algorithms, and develops technical reports. They work with large datasets to drive business improvements and enhance patient lifetime value.

Analyze complex datasets and apply statistical, quantitative, qualitative, AI/ML, NLP, geospatial, and research methodologies to support national security missions. Develop automated workflows, models, dashboards, visualizations, and analytical tools; manage datasets and metadata; assess information environment and irregular warfare activities; and communicate actionable findings to operational leaders. Collaborate with intelligence, operations, research, and technology teams in classified environments.

Lead data scientist role to analyze structured and unstructured data, develop algorithms and mathematical models, build scalable ETL/ELT pipelines, design RDBMS solutions, create data visualizations, and operationalize data platforms while working with cross-functional teams to deliver analytic products for clients.

Analyze large structured and unstructured datasets to develop algorithms, mathematical models, and scalable ETL/ELT pipelines. Build data products, visualizations, and tools to deliver insights to clients; collaborate cross-functionally and follow SDLC, testing, and version control practices. Must hold TS/SCI clearance and a bachelor’s degree.

Develop production-ready generative AI and machine learning solutions using Python, LLMs, and agentic AI frameworks. Build scalable AI agents, models, pipelines, monitoring capabilities, and enterprise integrations for healthcare use cases. Collaborate with data scientists, engineers, MLOps and DevOps teams, product groups, and business stakeholders to deliver reliable, compliant, and responsible AI systems. Create reports, projections, models, and presentations to communicate results and capabilities.

Analyze complex structured and unstructured data, build and maintain data pipelines, develop predictive and prescriptive models, and apply machine learning, NLP, statistical, and data engineering techniques. The role includes using cloud and distributed processing platforms, creating visualizations, communicating insights to nontechnical audiences, and delivering technical training to SOCOM personnel. Travel up to 25% is required.

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.

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