Top Data Science Jobs in Atlanta, GA

Collects, explores, and analyzes large datasets to identify business opportunities and improve outcomes. Develops predictive models, machine-learning algorithms, testing frameworks, and data pipelines; evaluates data quality and model performance; and communicates actionable insights to stakeholders and clients. The role also manages data science projects, establishes reporting best practices, and collaborates with cross-functional teams to implement and monitor solutions.

Lead design and deployment of advanced machine learning models and predictive analytics. Collaborate with product, engineering, and business teams to define projects, build data pipelines, perform exploratory analysis, and communicate insights. Mentor junior data scientists, evaluate statistical methods, and contribute to strategic data science direction to drive product and service improvements.

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.

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.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead end-to-end development and deployment of predictive and hybrid deep learning/LLM models for insurance use cases. Prototype, validate, and productionize models using modern ML frameworks and MLOps, collaborate with product and engineering, and communicate technical results to stakeholders to drive measurable business impact.

Lead and deliver productionized ML solutions across audience, marketing, pharma direct, and employer analytics. Design segmentation, propensity, attribution, and identity models; apply causal inference and experimentation; operationalize models with MLOps; evaluate generative AI/LLM use cases; set technical roadmap and mentor data scientists to drive measurable business impact.

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.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Build and optimize machine learning models, analyze structured and unstructured data, perform feature engineering and evaluation, and identify insights for business decisions. Create dashboards and data visualizations, communicate findings to technical and non-technical stakeholders, improve data quality, and maintain data integrity and confidentiality in regulated healthcare-related environments.

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.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Lead data science projects to identify growth and efficiency opportunities, design and deploy ML/DL models, define data acquisition strategies, mentor junior data scientists, manage stakeholders, and help build AI/ML capabilities for Honeywell Forge on cloud and distributed compute platforms.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

The Data Scientist II will oversee data management, design study protocols, train staff, lead report development, and manage client activities using project frameworks.

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.

Lead and execute advanced data science projects: identify opportunities, define data acquisition, build and deploy ML/DL models on cloud and containerized pipelines, mentor junior data scientists, manage stakeholders, and help shape enterprise AI/ML strategy for Honeywell Forge.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Design, build, and validate end-to-end ML models and AI/NLP solutions for pharmacy, claims, and clinical data. Responsibilities include feature engineering, explainability (SHAP), entity resolution, data quality, compliance-driven reporting, prototype front ends (Streamlit/BI), and collaboration with engineering, clinical, and client-success teams to productionize analytical outputs.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Own analysis and algorithmic solutions for ads delivery; train, deploy, and scale ML/optimization models into production; build logging, monitoring, and lineage; improve data pipelines; mentor data scientists; use agentic AI tools daily; collaborate with engineers to integrate models into ads systems and handle on-call production incidents.

Own the end-to-end analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.