Top Data Science Jobs in Atlanta, GA

Lead design, development, and deployment of AI/ML models (forecasting, optimization, RL, generative AI) for pricing and promotion. Extract and integrate complex data, monitor model performance, establish AI engineering patterns, document code, and communicate results to technical and non-technical stakeholders to operationalize models across the supply chain.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Mid-level Data Scientist focused on people analytics who designs, prototypes, and productionizes AI/ML solutions (including LLM-powered apps) to solve workforce and talent problems. Partners with HR, talent, operations, and engineering to build end-to-end data products, ensure responsible AI, and drive adoption and measurement of analytics solutions.

Early-career data professional responsible for collecting, cleaning, and analyzing data, performing EDA, developing reports and dashboards, supporting basic predictive models, and presenting insights to stakeholders.

Design, develop, and validate statistical and machine learning models for auto insurance risk using large, distributed datasets. Partner with product and vertical teams to create new solutions, support existing models, deliver actionable insights, lead small projects, and mentor junior staff. Use Python and analytic tools to translate analyses into business recommendations.

Lead enterprise data science and AI initiatives: identify high-value opportunities, design and deploy production ML/LLM solutions, oversee full model lifecycles, mentor teams, set standards, and manage strategic projects with vendors and stakeholders.

Design, develop, and deploy AI/ML models and data-driven solutions for Honeywell business groups. Collaborate with data engineers and architects to integrate models into production, define data acquisition strategies, govern analytics strategy, and promote AI/ML best practices across the Forge platform.

The role involves building and scaling a Closed-Loop Design Program that enhances pre-sales engineering through data science and analytics in supply chain automation.

Perform quantitative real estate and urban economic analysis, applying econometric and spatial methods to synthesize property, macroeconomic, and geospatial data. Build and maintain automated data pipelines, ensure data integrity, document dataflows, and prototype ML/AI methods. Collaborate with research, GIS, and engineering teams to produce reproducible insights and analytical products for stakeholders.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

The Data Scientist will conduct data research, develop analytics models, ensure data quality, and communicate actionable insights to stakeholders. Responsibilities include statistical analysis, data mining, and collaboration with business experts to enhance decision-making.

The Senior Director, Data Scientist will lead data science projects, manage data pipelines, implement data strategies, and mentor teams while influencing business strategy with data insights.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Designs, develops, and updates statistical and machine learning models; automates analytical processes; documents assumptions and code; extracts data via SQL; participates in model/code reviews and communicates results to stakeholders.

Build and deploy predictive models and applied LLM solutions to support claims operations (fraud, subrogation, damage severity, triage, quality). Automate modeling pipelines, improve internal modeling capabilities and data infrastructure, monitor deployed solutions, and communicate technical results to non-technical stakeholders.

Build and evaluate risk models for natural catastrophes and other perils using advanced statistics and machine learning. Analyze large, diverse datasets, validate vendor models, develop internal exposure views, create scalable pipelines, and communicate results to underwriters and stakeholders to inform underwriting decisions.

Lead design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Design and build ETL pipelines and data integrations, develop LLM-powered RAG systems and AI Agents, manage vector databases, run evaluations and fine-tuning, apply ML for recommendations and analytics, ensure data quality and A/B testing to drive product decisions.

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.