Top Remote Data Science Jobs in Palm Bay, FL

The Data Scientist II will work with sports tracking data to build models and analyses for NHL and NBA organizations, focusing on data transformation and metrics development.

The Applied Data Scientist III will develop scalable AI solutions, analyze data, and translate technical concepts for stakeholders while advocating for AI adoption.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Lead design of analytical models, metrics, and schemas that power an AI marketing orchestration layer. Translate marketer intuition into rigorous, production-ready metric models, partner with data engineering and AI teams to build pipelines and agent skills, and ensure metric accuracy for decisioning across paid and owned channels.

Develop, deploy, and operationalize analytical and ML/AI models for cybersecurity use cases. Collaborate with product and engineering teams to enable and migrate production workflows, work with databases and data architectures, create visualizations and reports, adopt new cloud orchestration and data virtualization technologies, and mentor junior cyber data scientists.

Lead data science projects to analyze complex dental and clinical datasets, build and validate statistical and predictive models, define KPIs, productionize ML with engineering/ML teams, communicate findings to stakeholders, and mentor junior data scientists to improve product and clinical impact.

The Senior Data Scientist will analyze and optimize marketing funnels, build statistical models, conduct experiments, collaborate with teams, and mentor junior members.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

Conduct original economic research using Stripe transaction and public datasets; apply econometric and causal methods; build reproducible data pipelines; create economic indicators; translate findings for product, policy, and external audiences; publish and present research; collaborate cross-functionally and with external partners.

This role involves partnering with sales to design data-driven solutions, engaging with customers, and supporting pre-sales engagements.

The Data Scientist will enhance statistical models for forecasting, analyze datasets for operational planning, and communicate insights to stakeholders.

As a Senior Data Scientist at Greenbox Capital, you'll build and scale predictive models, impact credit decisions, and collaborate across teams for data-driven solutions.

As a Staff Data Scientist, you'll develop and deploy machine learning models to enhance renter experiences and optimize property partnerships, collaborating across teams to translate data insights into actionable strategies.

Lead development and deployment of ML models and analytics for aircraft predictive maintenance. Build robust ETL pipelines, perform EDA and statistical analysis, collaborate with domain experts, integrate models into applications/APIs, and monitor model performance in production.

The Senior Data Scientist will lead data science strategy, develop models for lead qualification, fraud detection, and underwriting, and collaborate with product and engineering to enhance financial products.

The Senior Data Scientist will lead revenue-driving initiatives, optimize strategies, enhance data analysis techniques, and improve data pipelines for maximum revenue impact.

As a Data Scientist III, you'll build customer identity systems, develop recommendation models, predictive analytics for customer behavior, and collaborate with ML Ops and Data Engineering.

Lead development and deployment of machine learning and LLM models, analyze large datasets, build PowerBI dashboards, mentor junior data scientists, and present findings to leadership while automating processes and improving model performance.

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 design and deployment of reinforcement learning and sequential decision models for collections and recovery. Build scalable ML pipelines (Databricks/Spark), run experimentation and offline policy evaluation, collaborate with engineering/MLOps to productionize models, and mentor junior data scientists.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.