Top Senior Data Science Jobs in Miami, FL

Leads healthcare payer data science initiatives involving claims, membership, and operational data. Develops statistical models, predictive algorithms, and machine learning solutions for risk adjustment, utilization, and population health. Advises clients on payer modernization and operating model improvements, translates findings into visualizations and recommendations, coordinates multidisciplinary workstreams, validates data and models, and coaches analytical teams through complex client-facing projects.

Develop and productionize data science solutions for aerospace supply chain, inventory, fleet, and forecasting optimization. Build data pipelines, machine learning models, APIs, dashboards, scenario-planning tools, and telemetry integrations. Deploy and monitor services using CI/CD, containerization, model governance, drift detection, and retraining workflows. Collaborate with cross-functional stakeholders, measure business impact, ensure data quality and reproducibility, and mentor engineers and analysts.

Develop and lead complex AI and data science solutions, shape AI technical strategy, research emerging technologies, resolve large-scale technical problems, and guide cross-functional teams. The role focuses on scalable data solutions, machine learning, deep learning, large language models, generative AI, NLP, computer vision, Python, and Vertex AI. It requires a bachelor's degree and at least five years of progressive data analytics and data science experience.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Develop predictive, prescriptive, forecasting, valuation, and portfolio-performance models supporting collections, acquisitions, and business strategy. Build production data pipelines, transformations, analytical workflows, data models, quality checks, and monitoring frameworks using SQL, Python, and/or R. Perform exploratory analysis, create dashboards and reports, translate business needs into technical solutions, and collaborate with analytics, engineering, and business stakeholders.

Develop advanced statistical and machine-learning solutions for fraud and other data-intensive business problems. Translate ambiguous needs into hypotheses, experiments, models, and prototypes; shape data strategies; evaluate emerging generative and agentic AI technologies; and partner with Product, Engineering, Analytics, and business teams to operationalize solutions. The role also includes mentoring junior team members and fostering experimentation and innovation.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Lead the architecture and development of scalable data science solutions for geospatial intelligence and multi-INT mission data. Build Python automation pipelines, machine learning models, statistical analyses, dashboards, and visualizations supporting national security operations. Serve as a technical subject matter expert, mentor data scientists, translate customer intelligence needs into technical requirements, and deliver production-ready solutions aligned with NGA and DNI standards.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Own end-to-end data science projects, including problem framing, data acquisition, modeling, evaluation, deployment, and monitoring. Build production machine learning pipelines, develop features, test and deploy models, monitor drift, and lead retraining or redesign. Translate research objectives into experiments, communicate results to technical and non-technical stakeholders, and mentor junior data scientists. The role spans multiple data domains and supports defense and commercial AI/ML applications.

Develop and productionize machine learning and AI systems across document intelligence, language, vision, and multimodal applications. Design datasets, models, training pipelines, and evaluation frameworks; fine-tune foundation models; run GPU experiments; and build reusable AI capabilities. Partner with engineering and product teams on deployment, monitoring, reliability, and improvement while communicating technical tradeoffs and delivering measurable business outcomes in regulated financial services.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.