Top Data Science Jobs in Raleigh, NC

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.

Build and deploy machine learning models for credit and fraud risk, analyze portfolio performance, monitor models in production, develop risk strategies, collaborate with engineering and cross-functional partners, and conduct high-impact ad-hoc analyses to inform business decisions.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.

Build production AI retrieval and knowledge systems for rare disease research at NIH. Model biomedical ontologies in PostgreSQL, develop semantic search, retrieval-augmented services, ranking, evaluation, and observability, and deliver user interfaces with Next.js, React, and TypeScript. Deploy and operate services in on-premises and HPC Kubernetes environments, including GPU inference. Collaborate with NIH staff, clinicians, and researchers, and contribute to scientific publications.

Build and operate agentic AI systems for rare disease research, including multi-turn workflows, structured and validated outputs, tool calling, prompt testing, evaluation suites, asynchronous execution, streaming, observability, and failure handling. Collaborate with NIH staff, clinicians, geneticists, researchers, and patient communities to translate needs into reliable data models and system behavior. Own components through deployment, establish AI engineering standards, and contribute to scientific manuscripts, abstracts, and conference posters.

Develop and deploy NLP and machine learning models for classification, sentiment analysis, entity extraction, information retrieval, and user intent detection across large-scale legal, financial, news, and business datasets. Design scalable data science pipelines, evaluate modern AI algorithms and tools, translate business needs into technical solutions, and collaborate with product and engineering teams. Mentor junior scientists and contribute to model deployment, monitoring, maintenance, and development best practices.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

Lead a small-to-medium data science team to design, develop, and deploy advanced AI/ML solutions (especially LLMs/Transformer models), mentor junior staff, define methodologies and governance, collaborate cross-functionally, and set team strategy and high standards for model development.

Lead analytic projects and teams, review and approve models, define frameworks, methodologies and governance, design LLM/RAG/agentic AI systems with knowledge-graph and embedding integration, ensure evaluation, cost-performance optimization, AI observability, and compliance-aware production deployment while mentoring staff and communicating with senior stakeholders.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.

Lead design and production of ML/Deep Learning and time-series forecasting models, architect and fine-tune LLM/GenAI solutions, build conversational and agentic interfaces, transition prototypes to scalable production, and provide technical leadership and mentorship across cross-functional teams.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead end-to-end machine learning and advanced analytics projects: frame problems, build and deploy predictive/prescriptive models, design ETL pipelines, create dashboards and A/B test metrics, and deliver actionable insights to business stakeholders to improve fleet, pricing, and ridesharing products.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

Lead end-to-end development of fraud and risk ML systems: define problems, engineer features, build and evaluate models, collaborate with product and engineering, deploy with ML engineers, and monitor production performance to detect drift and trigger retraining.

Design, build, evaluate, and deploy predictive ML models across regression, classification, NLP, computer vision, and time-series problems. Perform data cleaning, feature engineering, EDA, model selection, tuning, explanation (SHAP, feature importance), and collaborate with engineers and stakeholders to operationalize and monitor models in production.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

As a Data Scientist, you will develop predictive models, analyze geoscientific data, and collaborate with geoscientists to improve mineral exploration efforts.

Develop autonomous AI agents capable of goal setting, decomposition, planning, tool use, memory, and long-horizon execution. Build evaluation frameworks for alignment, reliability, and safety; identify goal misalignment and undesirable behaviors; and integrate agent systems with APIs, databases, search engines, and other tools. Responsibilities include designing production-ready machine learning systems, collaborating across teams, and advancing agent autonomy through planning, causal reasoning, model-based reinforcement learning, and language modeling.

Owns the end-to-end development of cybersecurity vulnerability and exploit prediction models, including problem framing, feature engineering, training, evaluation, deployment, calibration, monitoring, and drift detection. The role addresses extreme class imbalance, hierarchical modeling, cold-start customers, and uncertainty quantification. It also requires explaining model outputs to security executives, publishing methodologies and benchmarks, and partnering with engineering to deploy reliable production systems.

Lead analytics and forecasting for the Fortnite ecosystem: maintain core reporting, build time series forecasts and department-wide data models, perform executive analyses, ensure data quality, support self-service pipelines, and collaborate with engineering and product to drive data-driven decisions.

Lead analytics to inform Finance and Product for the Fortnite ecosystem. Maintain core reporting and forecasting, build time-series models, develop shared data models, perform executive analyses, ensure data quality, support self-service pipelines, and collaborate cross-functionally to improve data-driven decision-making.

Own end-to-end data science for a priority vertical (starting with Insurance): frame business problems, build and validate models, partner with ML engineering for deployment, monitor live ROAS performance, and drive measurable revenue and lead quality improvements.