Top Senior Data Science Jobs in Atlanta, GA

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.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Analyzes financial and ERP data using statistical analysis, data mining, predictive modeling, and advanced algorithms. Supports Oracle financial shared services, data conversion, business continuity, disaster recovery, and contingency planning. Develops analytical outputs that improve agency KPIs and identifies trends and business opportunities. Requires collaboration with systems, network, hosting, and disaster recovery teams, plus participation in COOP exercises and Oracle environment upgrades.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

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.

Leads advanced data science and machine learning initiatives for healthcare payment integrity, including fraud, waste, and abuse detection, anomaly identification, predictive modeling, statistical analysis, dashboards, and recovery investigations. Provides technical leadership, mentors analysts, integrates production analytical solutions, collaborates with legal and business stakeholders, and presents findings to leadership. Requires healthcare expertise, advanced SQL and programming skills, and knowledge of claims, billing, reimbursement, and program integrity.

Build Boulevard’s Product Intelligence function by developing product data foundations, pipelines, dashboards, experimentation practices, and customer behavior models. Partner with Product, Engineering, and leadership to deliver actionable insights, self-serve analytics, and productionized models. Establish scalable processes, instrumentation standards, statistical workflows, and data-driven decision-making across the organization.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.

Lead data science work embedded in the newsroom: identify high-impact problems, build and productionize predictive and explanatory models, own core metrics/measurement, apply causal inference and experimentation, translate findings for senior stakeholders, and mentor junior data team members to improve analytic standards and adoption.

Own the scientific and product roadmap for monetization data products, including audience creation, expansion, causal measurement, and audience improvement. Lead cross-functional delivery with Product, Engineering, Sales, and GTM; define measurement standards, evaluate trade-offs, support customer pilots, influence commercial decisions, and turn bespoke studies into scalable products. Remain hands-on in SQL, Python, statistical modeling, and causal inference while mentoring scientists and establishing reusable methods.

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Build and deploy production-ready data science models, monitor model performance and drift, develop data science metrics and features, and translate analytical findings into actionable guidance. The role partners with stakeholders to frame business questions, optimize data collection, create data assets, identify gaps, and deliver insights supporting pediatric healthcare strategy and patient care.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Develop scalable data pipelines, statistical models, machine learning solutions, AI agents, dashboards, and decision-support tools for incentive compensation planning. Analyze large datasets, design quota and compensation models, evaluate business outcomes, ensure governance, and communicate recommendations to Finance, Engineering, product, sales, and executive stakeholders. The role also involves coding, debugging, automation, scenario modeling, and leading cross-functional analytical initiatives.

Senior Data Scientist partnering with Wealth Management and Private Banking to drive acquisition, retention, and AUM growth. Responsibilities include developing and deploying predictive models, preparing and analyzing large datasets, measuring cross-channel marketing campaigns, standardizing KPIs, and translating findings into actionable recommendations. The role requires collaboration with business, marketing, technology, risk, and compliance stakeholders, along with clear communication of insights to nontechnical audiences.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

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.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

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.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Build and deploy mechanistic, hybrid, and data-driven models for pharmaceutical CMC processes, including drug substance, drug product, packaging, labeling, technology transfer, and manufacturing. Integrate process and experimental data into FAIR data products, analytics, dashboards, and AI-enabled tools. Partner with technical teams and CDMOs to support process understanding, forecasting, risk assessment, regulatory filings, and technology-transfer decisions while owning the digital roadmap and mentoring colleagues.