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.

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.

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.

Serve as the analytics and data science partner for Workday Data Cloud, connecting demand, sales pipeline, product usage, and consumption-based pricing. Investigate ambiguous business questions, derive decision-ready insights, create visualizations and executive narratives, and advise senior stakeholders. Build curated data assets, improve data connections and pipelines, address instrumentation gaps, and collaborate with Product, Engineering, and GTM teams to support strategic growth and data-driven decision-making.

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.

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.

Develops statistical, machine learning, predictive, and advanced analytical solutions using structured and unstructured public health data. Integrates and analyzes data from surveillance, research, and administrative sources; evaluates data quality; creates visualizations and reports; monitors analytical tools; and translates findings into actionable public health insights. Collaborates with multidisciplinary CDC program teams and communicates technical results to technical and non-technical audiences.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Analyzes large-scale financial transaction data to identify patterns, anomalies, revenue opportunities, and risk-management insights. Supports THOR data product development, feature engineering, quality assurance, testing, documentation, production validation, and user support. Uses SQL, SAS, Teradata, statistical methods, and machine learning techniques while collaborating with technical and business stakeholders. The role also contributes to data visualization, presentations, training materials, and analytical solutions supporting regulatory compliance and operational efficiency.

Develop and deploy data science, machine learning, and AI solutions for risk, compliance, technology, and operational challenges. Analyze large datasets, build quantitative models and GenAI applications, document and test analytical solutions, and communicate findings to stakeholders. The role uses Python, SQL, LLM frameworks, Agile practices, CI/CD, and modern software engineering techniques while supporting model governance, risk management, automation, and enterprise analytics.

Supports AI and model development across Bank of America, applying data science, machine learning, statistics, and analytics to business and risk challenges. Responsibilities include developing scalable solutions, streamlining processes, documenting requirements, assessing AI governance, monitoring models, identifying risks, and communicating recommendations to technical and business stakeholders. The role also promotes AI technology reuse, compliance, automation, and measurable business value throughout the product lifecycle.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Staff and Principal Product Data Scientists will use advanced analytics, experimentation, causal inference, machine learning, and large-scale data technologies to guide Slack product strategy. They will define success metrics, build self-service reporting and data models, evaluate product adoption drivers, identify growth opportunities, and help develop Slack’s agentic data delivery platform. The role partners closely with Product, Design, Engineering, Product Research, and go-to-market teams while communicating insights to executives and cross-functional stakeholders.

Develops machine learning, generative AI, statistical modeling, and analytics solutions for business challenges. Responsibilities include data engineering, complex SQL, predictive modeling, automation, cloud deployment, visualization, stakeholder communication, mentoring junior data scientists, and independent AI research. The role partners with engineering, IT, product, and business teams to operationalize scalable AI and analytical solutions.

Leads enterprise cybersecurity architecture, risk assessment, access control, network security, incident response, and security technology strategy. Develops machine learning models, detection analytics, behavioral algorithms, and predictive models for threat, fraud, and insider-risk detection. Performs threat hunting across enterprise telemetry and provides technical escalation support. Requires extensive systems administration and information security experience, with preferred expertise in cybersecurity analytics, SIEM, EDR, UEBA, programming, cloud analytics, and automation.

Prototype and validate analytical methods and LLM applications for political, media, and advertising data. Build proof-of-concept tools, datasets, pipelines, and applications, then engineer validated methods toward production. Apply statistical and data science techniques, maintain rigorous software development practices, communicate findings clearly, and collaborate with technical and non-technical stakeholders.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

Own the company-wide revenue retention and add-on monetization analytics strategy. Build causal inference and retention models covering churn, MRR, GRR, NRR, attach, and usage. Partner with Finance, RevOps, Customer Success, CPaaS, AI, and product leaders to establish governed metrics, identify opportunities, guide experimentation, and influence product, pricing, and lifecycle decisions. Set analytical standards, develop reusable methods, and provide technical direction across teams.

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

Analyze large and complex datasets, build statistical and machine learning models, develop data workflows, enrich datasets, and create analytical solutions for business stakeholders. The role includes writing SQL and Python scripts, optimizing model performance, supporting dashboard development, collaborating with data and BI teams, and documenting models and pipelines. This is a fully remote U.S. position focused on delivering analytics applications through Seek’s cloud platform.