Top Remote Data Science Jobs in Raleigh, NC

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.

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.

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.

The role involves programming and data review for real-world data analysis, supporting epidemiologists, and managing complex data sets using statistical programming languages and tools.

Lead end-to-end data science projects including predictive modeling, algorithm design, and data pipelines. Build campaign-focused models (turnout, partisanship), perform exploration and visualization, translate insights into memos/presentations, collaborate with engineering to productionize models, mentor junior data scientists, and prototype emerging AI solutions for product impact.

Design, build, and own large-scale data pipelines and ML data curation for multimodal (text, image, audio, video) model training. Partner with research teams to ingest, label, filter, augment, store, and ensure dataset quality, compliance, and scalability. Optimize distributed data processing for training, prototype dataset tools, and integrate research advances into production systems.

The Senior Data Scientist will lead innovative data science efforts, leveraging advanced analytics, visualization, and insights from complex datasets to boost productivity.

Build marketing data science models and decision systems for spend allocation, channel measurement, forecasting, experimentation, and growth optimization. Unify marketing, retail, revenue, and product data; develop dashboards and analytical products; apply AI/ML and causal inference; and translate complex analysis into actionable recommendations for executives and cross-functional teams. The role also includes improving data quality, mentoring, documentation, and technical leadership.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

Design and implement advanced ML models for real-time forecasting and decision support, ensure data provenance and lineage, integrate federated geospatial and multi-format data, produce ROM and quarterly cost/resource reports, and apply Agile sprint practices to deliver mission-aligned analytics for DoD/IC customers.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.