Top Remote Data Science Jobs in Atlanta, GA

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

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.

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.

Six-month junior fixed-term role to design and develop LLM-based agents for Web3 scenarios, build token research and summarization agents, assist in trading agent development using market and on-chain data, and experiment with prompt engineering, memory, and multi-agent orchestration and evaluation.

Build and operationalize machine learning and AI solutions for hospitality pricing, forecasting, and revenue management. Analyze large datasets, write Python and SQL to prepare and validate data, and partner with product and engineering to turn analyses into product capabilities. Apply modern AI techniques, including LLMs and AI coding tools, and communicate insights to stakeholders.

Support IRS projects by developing, validating, and deploying predictive and statistical models on large tax and financial datasets. Perform data cleaning, feature engineering, anomaly detection, and risk scoring using Python/R, SQL, and Databricks. Communicate results to stakeholders and collaborate with cross-functional teams to inform audit prioritization, fraud detection, and compliance efforts.

Design, develop, validate, and deploy statistical and machine learning models to detect fraud, anomalies, and noncompliance in large tax and financial datasets. Clean and feature-engineer data, run robustness and sensitivity tests, collaborate with stakeholders, and present technical and non-technical findings. Support end-to-end data science lifecycle and productionization in a government environment.

Lead and support full-lifecycle data science projects (forecasting, NLP, classification/regression, optimization). Design production-ready solutions within client environments, consult with stakeholders to define ambiguous problems, communicate technical findings, and develop/teach bespoke analytics training.

Support IRS tax compliance and fraud detection by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, apply supervised and unsupervised learning (including NLP and graph analytics), collaborate with clients and teams, and deliver technical briefings and defensible, explainable model results.

Work with clients and technical staff to design, develop, and deploy predictive models and data-driven analytical solutions. Perform data extraction, ingestion, and modeling; evaluate and improve models; translate business rules into technical specifications and communicate results to stakeholders.

Support IRS analytics to detect fraud and non-compliance by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, perform feature engineering, apply supervised and unsupervised techniques (including NLP and graph analytics), collaborate with stakeholders, and deliver technical and non-technical briefings. Work across the full data science lifecycle and operate in secure government environments.

Develop, validate, and deploy predictive and statistical models using structured and unstructured tax and financial data to detect fraud and non-compliance. Clean and wrangle large datasets, perform feature engineering, apply supervised and unsupervised techniques, and communicate results to technical and non-technical stakeholders. Collaborate with clients and cross-functional teams and support model deployment and documentation in secure government environments.

Lead experimentation and decision science initiatives from problem definition through recommendations. Design and analyze A/B tests, quasi-experiments, matched-market tests, and causal inference studies. Apply advanced statistics, regression, predictive modeling, machine learning, and big-data analysis using Python, R, SQL, and PySpark. Partner with stakeholders, communicate actionable findings, build reproducible workflows, and mentor analysts and junior data scientists.

Leads the design, development, deployment, and monitoring of advanced statistical and machine learning models for insurance pricing, underwriting, claims, fraud, retention, and growth. Advises executives and actuarial teams, establishes modeling and experimentation standards, ensures responsible and scalable productionization, and mentors data scientists. The role also advances MLOps, AI infrastructure, model lifecycle management, explainability, fairness, and data science strategy.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

Senior individual contributor owning complex end-to-end ML problems for supply chain and consumer applications. Build and deploy predictive models (demand forecasting, delivery ETA, recommendations), embed with engineering teams, define MLOps standards, and advise leadership on data science strategy and investments.

The Data Scientist at GenLogs will build machine-learning systems, analyze large datasets, and collaborate with engineering and product teams to deliver advanced logistics intelligence solutions.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

The Marketing Data Scientist will analyze data, onboard clients, develop data pipelines in R, and propose improvements to enhance efficiency.

As a Data Scientist, you will analyze blockchain data to derive insights, develop and deploy predictive models, and improve product offerings.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Develop and validate SDTM and ADaM datasets using R per CDISC standards, support TLF generation in R/SAS, perform QC and reconciliation, collaborate with statisticians and data teams, contribute to automation, documentation, and version control.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

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.