Top Senior Data Science Jobs in Atlanta, GA

Design, build, and validate agentic AI systems and multi-agent workflows for financial use cases. Lead cross-functional teams to productionize models, create evaluation frameworks and gold-standard datasets, develop reusable agent patterns and prompts, integrate AI into services, and implement systematic testing to meet compliance and reliability standards.

As a Senior Data Scientist at Omnissa, you will lead the data science team to innovate AI/ML solutions, develop advanced analytics models, and collaborate on integrating machine learning into core platform features.

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 Data Scientist will optimize global shipping using data science to build predictive models, create dashboards, and enhance operational performance. Requires strong skills in AI and collaboration.

Lead customer-facing data science engagements: manage data acquisition/cleaning, develop and evaluate scalable models, drive modeling best practices, contribute thought leadership, write extensible code, and define solution and business strategy with cross-functional teams.

Lead customer-facing data science engagements: acquire and prepare data, evaluate and operationalize models at scale, drive modeling best practices, research industry trends, write extensible code, and define solution and customer strategy while promoting ethics and privacy.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Develop frameworks and tooling to estimate long-term impacts of product changes from short-term experiment outcomes. Build causal inference methods (experimental, econometric, quasi-experimental), evaluate heterogeneous treatment effects, own major estimation problems, and communicate results to technical and non-technical stakeholders to drive better long-term decisions.

Lead high‑impact data science initiatives across Global Operations, building production‑grade analytics, models, and dashboards (primarily in R), using Snowflake and Posit platforms, applying advanced statistics, ML, and LLM techniques, mentoring junior staff, and presenting insights to senior leadership to reduce supply chain risk and optimize manufacturing.

Lead design, development, deployment, validation, and maintenance of data attributes and features. Convert analytic code to KEL, troubleshoot and resolve complex data/code issues, test and monitor attribute quality, and mentor junior team members while collaborating with modeling, technology, product, and internal stakeholders.

Lead end-to-end AI/ML projects for Finance Operations: extract and prepare large datasets, engineer features, build and tune models (including generative AI), deploy and monitor production models, create stakeholder visualizations, and collaborate cross-functionally to drive business insights and automation.

Design, build, and deploy agentic LLM-powered systems using RAG, tool orchestration, and model adaptation. Implement evaluation, monitoring, governance, and MLOps for production GenAI solutions, collaborate cross-functionally, and mentor junior engineers.

Lead end-to-end data science for a priority vertical (Insurance), building, validating, and deploying ML models that drive ROAS and lead quality. Partner with stakeholders and ML engineering to monitor live performance, iterate models, and identify new modeling opportunities to improve revenue and media efficiency.

Senior individual-contributor data scientist partnering with Product, Operations, Finance and executives to run advanced analytics, design experiments, build predictive models and dashboards, develop ETL/ELT pipelines, and translate insights into business recommendations to improve customer experience, operational efficiency, and performance.

The Senior Data Scientist will build frameworks for data-driven decision making, develop predictive models, ensure data quality, and enhance customer experiences through analytics and operational insights.

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

The Sr. Football Data Scientist will build, deploy, and refine analytical models for football insights, managing large datasets and collaborating with teams for high-quality analytics solutions.

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

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

Develop and refine predictive models, perform exploratory data analysis, and generate AI-driven insights for DoD missions. Integrate multi-source data, apply geospatial analytics, iterate models via Agile sprints, produce performance and cost reports, and optimize analytic workflows for operational deployment.

Apply advanced statistical and machine learning techniques to GEOINT problems, integrating tabular, geospatial, and unstructured data. Build and operationalize models (NLP, deep learning, image/object detection), develop scripts/microservices, maintain scalable data pipelines, enforce data governance, produce dashboards/reports, and operate within TS/SCI security and DoD/IC policies to support Army INSCOM mission objectives.

Lead Revenue Analytics by developing data-driven strategies to maximize revenue, own and improve data pipelines, run advanced experiments and causal analyses, lead cross-functional initiatives, mentor junior analysts, and embed AI tools into workflows to scale impact.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.