Top Data Science Jobs in Atlanta, GA

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Supports data scientists with data exploration, cleaning, analysis, model development, validation, visualization, and prototypes. Uses Python and SQL while learning machine learning, generative AI, cloud data platforms, and production data science practices. Participates in reviews, documentation, presentations, and collaborative projects across multiple co-op terms, progressively increasing responsibility under mentorship.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Designs, builds, and operationalizes production data science, machine learning, generative AI, and LLM systems. Responsibilities include data and AI pipelines, predictive models, RAG applications, experimentation, MLOps, model serving, monitoring, governance, cloud migration, and business analytics. The role partners with Product, Engineering, BI, and Data teams to translate business problems into scalable AI solutions and measurable product or operational improvements.

Develops and evaluates machine learning and generative AI solutions, builds proof-of-concept applications, integrates data from multiple sources, creates modeling-ready datasets, constructs predictive models, and communicates actionable insights to leadership. The role also supports automation, solution integration, mentoring junior data scientists, and collaboration with cross-functional teams. This hybrid campus-based position requires onsite work three days per week.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Data Science Interns will learn to extract, transform, cleanse, and analyze data from multiple sources; evaluate machine learning results; measure marketing effectiveness; forecast activities; and produce analytical reports. The role emphasizes research, quantitative analysis, communication, project prioritization, and collaboration with mentors and senior leaders.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Develops data science solutions using structured and unstructured data, machine learning, natural language processing, and production data pipelines. Responsibilities include data exploration, cleaning, analysis, visualization, mining, governance, quality monitoring, security, and stakeholder collaboration. The role translates complex data into actionable insights and supports cloud-based applications and client decision-making. A bachelor's degree and eligibility for Public Trust or government suitability determination are required.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Perform epidemiologic analyses on surveillance, clinical, claims, lab, and survey data; develop analysis plans, statistical methods, and technical reports; build dashboards and automated analytic workflows; design data quality rules and surveillance indicators; translate findings for diverse stakeholders; support public health reporting, manuscripts, and dissemination; mentor junior staff and provide technical assistance.

Own the enterprise ontology and knowledge graph strategy across Gas Power domains. Establish OWL, RDF, SHACL, and SPARQL standards; resolve cross-domain modeling conflicts; scale training and delivery practices; govern model approval, versioning, lifecycle, access, metadata, and lineage; and ensure the knowledge graph supports GenAI, RAG, and agentic workflows. Serve as the final technical authority while mentoring teams and partnering with senior stakeholders on platform selection and roadmap decisions.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

Develop client-facing analytics, dashboards, data models, and large-scale data workflows while building and operationalizing internal AI capabilities. Design and productionize LLM-powered and agentic workflows, support AWS-based AI infrastructure, and implement integrations, access controls, monitoring, security, and responsible-AI practices. Partner with IT and practice teams to translate requirements into scalable solutions, document runbooks, monitor adoption and performance, and improve secure production use.

Leads data science initiatives by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Develops predictive models, algorithms, quantitative analyses, and machine learning or NLP solutions to address client challenges. Collaborates with clients, provides data-driven recommendations, leads complex projects and deliverables, mentors team members, and identifies opportunities to apply data science to public- and private-sector problems.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Develops and maintains enterprise data systems, models, schemas, metadata, governance processes, and data workflows supporting the U.S. Navy. Performs data quality validation, ingestion and transformation, forecasting, scenario analysis, and risk assessment. The role also automates data processing, recommends architecture improvements, collaborates with technical and nontechnical stakeholders, and provides expertise in enterprise data management.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Builds workforce analytics, visualizations, statistical analyses, time-series forecasts, and machine learning models using HR and clinical data. The role predicts staffing needs and employee attrition, analyzes pay and burnout relationships, supports pay equity and recruitment optimization, and translates HR strategies into measurable KPIs. Responsibilities include deploying models on cloud platforms, conducting experiments, protecting employee data, and communicating insights to technical and non-technical stakeholders.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.