Top Data Science Jobs in Miami, FL

Lead the fraud data science function: define ML strategy, build and deploy real-time fraud detection models, investigate fraud patterns (including lending fraud), run experiments, collaborate with stakeholders, and maintain monitoring and dashboards to minimize fraud losses.

Lead Fraud Data Scientist to define ML strategy, build and productionize fraud detection models (supervised and unsupervised), investigate emerging fraud patterns, run experiments, collaborate with Product/Engineering/Risk, and maintain dashboards tracking fraud KPIs.

Lead the fraud data science function: define ML strategy, build and productionize fraud and lending-fraud models, run advanced analyses (clustering, network analysis), design A/B tests, integrate ML with rules engines, monitor models in cloud, and communicate results to stakeholders.

Lead the fraud ML strategy and end-to-end development of real-time fraud detection models. Mentor data scientists, design credit/lending fraud models, perform unsupervised and network analyses, run A/B experiments, deploy and monitor models in cloud, and build dashboards to report KPIs and support cross-functional stakeholders.

The Staff Data Scientist will advance machine learning models to optimize claim audits and improve business outcomes in healthcare.

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.

Lead radiometric calibration and image processing for multispectral infrared instruments, commission FireSat satellites, and develop/execute SBEM calibration plans. Implement production-ready pipelines, perform R&D to improve calibration, write production-quality code, and collaborate cross-functionally to maintain operational remote sensing data products and services.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.

The Forward Deployed Marketing Data Scientist will work with AI customers and engineering teams to optimize AI-driven marketing campaigns through data analysis, diagnostics, and communication of insights to stakeholders.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

Use statistical and causal inference methods to build, implement, and monitor marketing models (Bayesian MMM, multi-touch attribution), analyze A/B and uplift tests, partner with marketing to test and drive data-informed decisions from large web-product datasets.

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.

Analyze large-scale educational data to inform product design and efficacy. Develop, evaluate, and maintain statistical and AI models, collaborate with cross-functional teams, communicate findings to stakeholders, and leverage AI tools for analysis and feature engineering while following software development practices.

Develop and deploy machine learning models for demand forecasting and inventory optimization; conduct advanced time-series analysis; collaborate with analytics engineers and data analysts to build end-to-end data products; translate analysis into actionable recommendations for supply chain; promote data science best practices and mentor the team.

Drive product and business analyses across Quora: design and evaluate experiments, build models and visualizations, analyze cross-product data to surface root causes, and partner with cross-functional teams to implement data-informed solutions and frameworks.

Lead the technical development and automation of enrollment forecasting models, collaborating with various departments to enhance decision-making and forecast accuracy.

Lead applied AI projects end-to-end: prototype, validate, and help deploy ML and GenAI solutions (malicious behavior, anomaly/fraud detection, developer experiences). Act as internal AI consultant, design experiments and evaluations, bridge research and production, mentor peers, and ensure data governance and privacy.

Design and deploy ML systems for enterprise search, recommendations, retrieval, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and metrics. Apply statistics, experimentation, and causal inference to guide product decisions. Collaborate cross-functionally, prototype LLM-based capabilities (embeddings, RAG), and mentor junior team members.

Lead design and deployment of ML systems for enterprise search, retrieval, recommendations, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and KPIs. Apply statistics, causal inference, experimentation, and analysis to guide product strategy. Prototype and productionize LLM-based features, mentor junior staff, and collaborate cross-functionally.

Design and implement data-centric cybersecurity solutions: develop ML algorithms and automation for security tools; discover, parse, map, and transform legacy RMF artifacts into OSCAL; migrate and analyze security data; reproduce dashboards/workflows in eGRC; collaborate with Professional Services; produce migration assessments and reports.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.