Top Remote Data Science Jobs in Palm Bay, FL

Design, build, and productionize generative AI solutions: advanced Python development, LLM prompt engineering and fine-tuning, agent frameworks, RAG pipelines with vector DBs, API and cloud deployment, MLOps/LLMOps, model evaluation and Responsible AI practices, and translate business needs into scalable AI applications.

Build, optimize, and scale production Generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and deploy LLMs, and create high-performance FastAPI async backends. Improve latency, cost, and reliability, containerize services with Docker, and manage monitoring, API security, and CI/CD for ML deployments.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Design, build, and optimize generative AI, RAG, and agentic RAG systems for drafting, search, and reasoning. Improve LLM outputs via prompt design, retrieval optimization, embeddings, reranking, and evaluation. Develop production-grade ML components and retrieval pipelines using OpenSearch/Solr, write maintainable Python code, and collaborate with engineering, product, and domain teams to improve relevance, accuracy, and user trust.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

The Data Scientist designs and builds data structures for AI applications, manages vector and feature stores, leads data discovery, and collaborates on responsible data use while ensuring high-quality, governed datasets for ML models and applications.

Lead analytical efforts in Real World Data research, develop specifications and methodologies, manage projects, and communicate findings to stakeholders.

Design, build, and deploy search and recommendation ML systems (ranking, retrieval, personalization, query understanding) using embeddings and LLM approaches. Collaborate with engineers and product to create scalable data pipelines, integrate models via APIs, run A/B tests, and monitor online/offline metrics to improve relevance and engagement.

Lead data science across Growth and Membership Operations: define problems, build scalable data models and pipelines, develop analytical and ML solutions, partner cross-functionally, and integrate AI tools to optimize sales, marketing, telesales, and enrollment operations.

As a Senior Data Scientist, you will develop machine learning models, optimize deployment processes, and collaborate with cross-functional teams to enhance clinical AI workflows.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.

The Senior Associate Scientist will analyze and visualize data, collaborate on screening strategies, and validate hits for drug discovery using the EMMI platform.

This role involves leading data science projects, designing technical solutions, implementing machine learning methodologies, and developing data workflows for cybersecurity applications.

The Data Scientist II will develop machine learning algorithms, architect data solutions, and support cybersecurity initiatives by analyzing complex data sets.

Lead advanced analytics and ML for partner-facing teams: build end-to-end predictive models (churn, lead scoring, health scores), marketing attribution and mix models, and maintain ML lifecycle standards. Develop agentic workflows and Claude Skills to accelerate work, mentor analytics team, and translate model outputs into actionable insights for Sales, Marketing, and CX.

Develop and validate SDTM and ADaM datasets using R per CDISC standards, support TLF generation in R or SAS, perform QC and reconciliation, collaborate with biostatistics and clinical teams, contribute to automation, documentation, and version control for regulatory-compliant clinical trial deliverables.

Design, implement, and evaluate advanced NLP/NLU systems for clinical text using transformer and research-oriented neural architectures. Prepare and enrich clinical datasets, run experiments and error analyses, mitigate model risks (bias, drift, hallucination), and partner with ML engineers to deploy models into a compliant cloud environment.

The Senior Data Scientist will design and deploy machine learning models, analyze complex datasets, develop analytical solutions, and mentor junior team members in a remote environment.

Lead retention and churn efforts by building survival and lifecycle-state models, classifying churn reasons, creating personalized win-back and next-best-action interventions, optimizing offers/promos, designing uplift experiments, and owning production model lifecycle, monitoring, and MLOps across Product, CRM, Marketing, and Engineering.

Lead Data Scientist responsible for creating ML-driven risk assessments and AI workflows within a SaaS security platform, ensuring effective decision-making and system reliability.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

The Senior Data Scientist leads data-driven initiatives, optimizing analytics for business decisions, mentoring staff, and ensuring data quality across healthcare projects.