Top Remote Data Science Jobs in Pensacola, FL

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.

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.

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile 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.

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.

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.

The IT Subject Matter Expert will provide technical expertise and support for enterprise IT initiatives, governance, and modernization efforts within a federal environment, requiring strong analytical and problem-solving skills.

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Build and deploy machine learning models for credit and fraud risk, analyze portfolio performance, monitor models in production, develop risk strategies, collaborate with engineering and cross-functional partners, and conduct high-impact ad-hoc analyses to inform business decisions.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.

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.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

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.