Top Data Science Jobs in Miami, FL

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.

Analyze complex datasets, build predictive models, and communicate findings to stakeholders while collaborating with data engineers and quantitative researchers.

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.

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.

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 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 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.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Lead Fraud Data Scientist to define ML strategy, build and productionize fraud detection models (including lending fraud), run advanced analyses and A/B tests, mentor the team, integrate ML with rule engines, and create dashboards to monitor fraud KPIs and model performance.

Lead the fraud data science function: define ML strategy, build and productionize fraud detection models, investigate fraud patterns, run experiments, collaborate with Product/Engineering/Risk, and deliver dashboards and monitoring to minimize fraud losses while preserving customer experience.

Lead the fraud data science effort: define ML strategy, build and productionize real-time fraud detection models, investigate fraud patterns with unsupervised and graph techniques, run experiments, collaborate with product/engineering/risk, and maintain dashboards to monitor model performance and fraud KPIs.

Lead analytics and AI/ML initiatives for Financial Crime Risk Management. Work with large complex datasets to build ML and LLM-driven solutions, support regulatory and audit requests, perform ad hoc analysis, validate and document results, and communicate insights to stakeholders across the enterprise.

Develop and monitor financial crime risk models using data wrangling, statistical inference, visualization, and advanced analytics. Build production-grade models (tree-based and neural networks) in cloud environments, translate business problems to data solutions, and collaborate with engineers and stakeholders to deliver scalable transaction monitoring and AML analytics.

Lead strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.

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

Design, build, and validate predictive models and forecasting algorithms for pricing, booking curves, cancellations, and upgrades. Prepare and ETL large datasets (Snowflake) using SQL and Python, calibrate RMS algorithms, run A/B tests and attribution, and create dashboards in Power BI or Tableau. Collaborate with data engineers and revenue managers to productionize models and translate results for stakeholders.

Lead design, build, and production deployment of generative AI products (LLM apps, agents, copilots, RAG/search). Rapidly prototype features, build APIs/services and retrieval pipelines, optimize performance, ensure responsible AI, and mentor team members while setting AI technical direction across the organization.

The Senior Data Scientist will enhance intelligence operations through advanced data analysis, machine learning, and support for GEOINT and multi-INT methodologies, requiring collaboration and leadership in complex problem-solving.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Design, build, and validate end-to-end ML models and AI/NLP solutions for pharmacy, claims, and clinical data. Responsibilities include feature engineering, explainability (SHAP), entity resolution, data quality, compliance-driven reporting, prototype front ends (Streamlit/BI), and collaboration with engineering, clinical, and client-success teams to productionize analytical outputs.

Lead design, deployment, and operation of production ML systems and analytics. Provide technical leadership for cloud-native ML infrastructure, data pipelines, model serving, monitoring, and MLOps. Translate business problems into data solutions, run model reviews, mentor data scientists, and drive process improvements and reliability for production models.

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.

Own analysis and algorithmic solutions for ads delivery; train, deploy, and scale ML/optimization models into production; build logging, monitoring, and lineage; improve data pipelines; mentor data scientists; use agentic AI tools daily; collaborate with engineers to integrate models into ads systems and handle on-call production incidents.

Develop frameworks and tooling to estimate long-term impacts of product changes from short-term experiment outcomes. Build causal inference methods (experimental, econometric, quasi-experimental), evaluate heterogeneous treatment effects, own major estimation problems, and communicate results to technical and non-technical stakeholders to drive better long-term decisions.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.