Top Remote Data Science Jobs in Memphis, TN

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and deploy machine learning and generative AI models (including LLMs and text-to-image). Analyze large datasets, run A/B and statistical tests, collaborate with data engineering and engineering teams, and create dashboards/visualizations to communicate insights. Optimize data pipelines and model performance to support data-driven decisions.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Design, validate, and deploy predictive models using claims, clinical, pharmacy, and lab data to improve population health, utilization management, and care quality. Partner with clinical and business teams, evaluate model performance, scale analytical approaches, and communicate insights to technical and non-technical stakeholders.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Design and validate clinical and biomarker data pipelines, perform source-to-target mappings (e.g., EHR to data model), extend ontologies and vocabularies, ensure data quality, evaluate informatics tools, and support data architecture and analysis workflows for a regulatory-grade platform.

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

Build and maintain analytics data models, dashboards, and reports. Use SQL (Postgres, Trino, Iceberg) and dbt to power financial metrics. Perform deep-dive analyses using SQL and Python (Jupyter, Pandas) to answer business questions, operationalize data, and improve reporting and automation.

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 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 will analyze customer data, design experiments, build models, and support data-driven decision-making across product, marketing, and customer experience teams.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Develop, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

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.

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

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Lead development of foundational marketing ML models (customer intent, next-best-action, marketing mix and attribution). Own model selection, explainability, monitoring, decisioning layers, and translate outputs into actionable recommendations for marketing and product stakeholders while providing technical leadership.

Lead development of Vanta's business analytics function: build scalable product data assets, define measurement frameworks, use AI-assisted analytics, partner with cross-functional teams, and deliver executive-level recommendations to drive product and business decisions.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Lead end-to-end AI/ML initiatives to improve student retention, engagement, and enrollment. Architect and operate data pipelines, feature stores, and production ML systems; deploy, monitor, and automate models; run experimentation programing and measure business outcomes. Mentor team members and set technical standards across Product, Engineering, and CX.

Lead cross-functional teams to identify and implement revenue-driving strategies. Own revenue optimization, develop quantitative analyses (A/B testing, causal impact), improve data pipelines and tracking, embed AI into workflows, and present insights to leadership to scale and recover revenue.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.