Top Senior Data Science Jobs in Tampa, FL

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.

The Senior Applied AI Data Scientist will build AI workflows, prototype applications, and enhance decision-support systems while collaborating with various teams to operationalize AI solutions.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Lead development and deployment of production-grade ML solutions. Partner with business and technology teams to build anomaly detection, NER, and LLM-based agentic AI, manage ML lifecycle, mentor mid-level data scientists, and ensure compliance and risk controls.

As a Staff Data Scientist, you will lead product strategy by building predictive models, optimizing matching algorithms, and guiding product innovations in a consumer tech environment.

As a Staff Data Scientist, you will drive growth through data science initiatives, optimize sales processes, and develop recommendation engines for strategic decisions. You will work closely with multiple teams and build models that enhance revenue opportunities.

As Staff Data Scientist, you will drive advanced analytics to optimize sales performance, build predictive models, and develop insights for decision-making across the GTM organization.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

Lead end-to-end client ML engagements: scope problems, design, build, and deploy production ML solutions across cloud and containerized stacks. Lead and mentor small teams, manage client relationships, evaluate emerging tools, and ensure models are production-ready and explainable to technical and non-technical stakeholders.

Design, build, and deploy ML models across diverse use cases; work with managed cloud ML platforms and containerized solutions; collaborate with engineers on production integration and MLOps best practices; preprocess and analyze large datasets; mentor teammates and influence technical direction; translate complex ML concepts for technical and non-technical stakeholders.

Lead end-to-end data science projects: develop and productionize models, build segmentation and performance models, run experiments/causal analyses, deliver insights to stakeholders, and mentor junior team members while contributing to scalable data assets and cross-functional collaboration.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

Develop a tool to enhance signature management capabilities and integrate diverse data holdings, leveraging AI/ML solutions to meet operational needs.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

Lead development and evolution of forecasting assets using statistical, ML, and AI-driven methods. Own model development, feature engineering, EDA, ensemble and hierarchical forecasting, and evaluation. Integrate foundation models and deep learning where beneficial, collaborate with stakeholders to drive measurable impact, and support deployment and operationalization of forecasting solutions within a shared Center of Excellence.

As a Senior Data Scientist, analyze healthcare data to improve health outcomes, develop reports and dashboards, and collaborate across teams to leverage genomic data for health insights.

The role involves building and validating Marketing Mix Models and geo-experiments, managing measurement infrastructure, and communicating insights to stakeholders.

Lead Data Scientist will support advanced analytics, predictive modeling, and machine learning initiatives for clinical decision-making in healthcare. Responsibilities include algorithm design and compliance with VA governance standards.

Design and validate clinical and biomarker data pipelines, perform source-to-target mappings (e.g., EHR to data model), extend ontologies and vocabularies, ensure data quality, evaluate informatics tools, and support data architecture and analysis workflows for a regulatory-grade platform.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Design, develop, and deploy time-series and machine-learning forecasting models (ARIMA, SARIMA, ETS, XGBoost, LightGBM) for demand planning, inventory, and sales forecasting. Perform large-scale SQL data processing, operationalize models in cloud environments (Azure preferred), present insights, and lead client workshops.

Develop, validate, and implement econometric and predictive credit risk models. Extract, clean, and analyze large datasets from relational and datalake sources. Monitor, backtest, and troubleshoot model performance, ensure adherence to credit policy, collaborate with business partners, document models, and support deployment including third-party vendor solutions.