Top Data Science Jobs in Atlanta, GA

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

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 productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

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 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.

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.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

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 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 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.

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.

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.

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.

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.

Design and maintain data models and pipelines for mortgage and lending platforms. Develop ML/AI, predictive and prescriptive analytics, and LLM/Generative AI solutions. Collaborate with engineering and business stakeholders to build scalable cloud and data warehouse solutions, ensure data quality and governance, and deliver dashboards and executive reporting.

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.

Lead quantitative analysis and modeling of large real-world health datasets to estimate disease prevalence and incidence. Design statistical and machine learning models, build data pipelines and dashboards, ensure reproducibility and data quality, collaborate with public health experts, and prepare scientific reports and presentations.

Lead epidemiology and surveillance programs, directing multidisciplinary teams to develop surveillance datasets, analytic products, dashboards, and reports. Provide scientific leadership on epidemiologic methods, statistical modeling, data quality assurance, and interpretation. Advise clients, support data modernization and AI-enabled analytics, lead dissemination and thought leadership, and contribute to business development and contract-dependent work.

The Senior Data Scientist partners with Customer Success to perform deep-dive analyses, diagnose metric shifts, build predictive models (churn/upsell), apply causal inference and experimentation, measure business impact, and develop AI/LLM solutions. Responsibilities include self-serving across the data stack for extraction, pipelining, and dashboards, defining KPIs, and delivering actionable insights to reduce churn, drive revenue, and improve operational efficiency.

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Lead applied AI projects end-to-end: prototype, validate, and help deploy ML and GenAI solutions (malicious behavior, anomaly/fraud detection, developer experiences). Act as internal AI consultant, design experiments and evaluations, bridge research and production, mentor peers, and ensure data governance and privacy.

Owner of analytics for EHR, revenue cycle, and operations: design and run experiments, analyze financial and operational metrics, investigate billing and account issues, and deliver actionable insights to Product, Operations, Finance, and Customer Experience to improve accuracy, reduce friction, and support growth.

Design and develop methods for analyzing big data to generate insights for healthcare applications, using statistical methods and machine learning. Collaborate across teams to improve predictive modeling and data utility.

Lead development of data-driven solutions: clean and preprocess large structured and unstructured datasets, engineer features, build and deploy supervised/unsupervised ML and deep learning models, perform advanced statistical analysis, collaborate with data engineers on MLOps/CI-CD pipelines, and present actionable findings to technical and nontechnical stakeholders.