Top Senior Data Science Jobs in Miami, FL

Develop and deploy deep learning models and petabyte-scale multimodal data curation pipelines for foundation-model training. Responsibilities include document extraction, OCR, layout and table analysis, deduplication, distributed processing, dataset and metric development, experimentation, production scaling, and deployment through NVIDIA Inference Microservices. The role also involves publishing research, creating technical documentation, communicating findings, and mentoring team members.

Build production AI retrieval and knowledge systems for rare disease research at NIH. Model biomedical ontologies in PostgreSQL, develop semantic search, retrieval-augmented services, ranking, evaluation, and observability, and deliver user interfaces with Next.js, React, and TypeScript. Deploy and operate services in on-premises and HPC Kubernetes environments, including GPU inference. Collaborate with NIH staff, clinicians, and researchers, and contribute to scientific publications.

Build and operate agentic AI systems for rare disease research, including multi-turn workflows, structured and validated outputs, tool calling, prompt testing, evaluation suites, asynchronous execution, streaming, observability, and failure handling. Collaborate with NIH staff, clinicians, geneticists, researchers, and patient communities to translate needs into reliable data models and system behavior. Own components through deployment, establish AI engineering standards, and contribute to scientific manuscripts, abstracts, and conference posters.

Develop and deploy NLP and machine learning models for classification, sentiment analysis, entity extraction, information retrieval, and user intent detection across large-scale legal, financial, news, and business datasets. Design scalable data science pipelines, evaluate modern AI algorithms and tools, translate business needs into technical solutions, and collaborate with product and engineering teams. Mentor junior scientists and contribute to model deployment, monitoring, maintenance, and development best practices.

Senior individual contributor responsible for leading pet/P&C insurance pricing model development (GLMs and ML), owning end-to-end projects, performing EDA and root-cause analysis, communicating results to stakeholders, and mentoring junior team members.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead end-to-end machine learning and advanced analytics projects: frame problems, build and deploy predictive/prescriptive models, design ETL pipelines, create dashboards and A/B test metrics, and deliver actionable insights to business stakeholders to improve fleet, pricing, and ridesharing products.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Lead end-to-end development, deployment, and governance of production risk and pricing models for real-time decisioning. Perform EDA, feature engineering, model building, validation, monitoring, A/B testing, and partner with DevOps/Product/Engineering to ship scalable ML services. Employ modern AI/LLM tooling and produce clear analyses for stakeholders.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

As a Data Scientist, you will develop predictive models, analyze geoscientific data, and collaborate with geoscientists to improve mineral exploration efforts.

Owns the end-to-end development of cybersecurity vulnerability and exploit prediction models, including problem framing, feature engineering, training, evaluation, deployment, calibration, monitoring, and drift detection. The role addresses extreme class imbalance, hierarchical modeling, cold-start customers, and uncertainty quantification. It also requires explaining model outputs to security executives, publishing methodologies and benchmarks, and partnering with engineering to deploy reliable production systems.

Provides biostatistical data science and analytics support for clinical development and evidence generation. Collaborates with biostatistics, data management, clinical development, and medical affairs teams; applies statistics, mathematics, AI, and innovative technologies to clinical trial and real-world data. Contributes to clinical trial design, statistical analysis plans, clinical study reports, publications, and protocol development while following ICH and FDA guidelines.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

Lead end-to-end data science projects including predictive modeling, algorithm design, and data pipelines. Build campaign-focused models (turnout, partisanship), perform exploration and visualization, translate insights into memos/presentations, collaborate with engineering to productionize models, mentor junior data scientists, and prototype emerging AI solutions for product impact.

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

Develop and deploy production ML models for document verification and fraud detection. Perform model evaluation, experimentation, and analytics; build monitoring, tooling, and automation; partner cross-functionally to improve product performance and operational efficiency.

Lead predictive modeling and causal inference initiatives for clients, developing models from exploratory analysis through production deployment, monitoring, and retraining. Apply GLMs, econometric methods, uplift modeling, propensity methods, and deep-learning forecasting. Process large-scale data with SQL and Spark, build models in Python, improve production reliability, and communicate findings to technical and business stakeholders in a consulting environment.

Build marketing data science models and decision systems for spend allocation, channel measurement, forecasting, experimentation, and growth optimization. Unify marketing, retail, revenue, and product data; develop dashboards and analytical products; apply AI/ML and causal inference; and translate complex analysis into actionable recommendations for executives and cross-functional teams. The role also includes improving data quality, mentoring, documentation, and technical leadership.

Conduct real-world data analyses using SAS or R and commercial claims and EHR databases. Build longitudinal study cohorts, define baseline and outcome variables, apply epidemiologic methods, perform statistical tests, regression and survival analyses, assess data quality, review protocols and statistical analysis plans, and program tables, figures, and listings. The role requires advanced biostatistics or epidemiology expertise and strong communication skills.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Design and implement advanced ML models for real-time forecasting and decision support, ensure data provenance and lineage, integrate federated geospatial and multi-format data, produce ROM and quarterly cost/resource reports, and apply Agile sprint practices to deliver mission-aligned analytics for DoD/IC customers.

Design, implement, and sustain metadata governance for a federated DoD data ecosystem. Develop automated metadata tagging and lineage tracking, integrate OpenLineage/Unity Catalog/ESRI systems, ensure DoD compliance and GIS metadata standards, and produce metadata compliance and lineage reports.