Top Senior Data Science Jobs in Raleigh, NC

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.

Lead AI/ML initiatives at Risepoint to develop predictive models for enhancing student engagement and retention, coordinating cross-functional teams to achieve measurable outcomes.

Lead development of graph-based identity resolution for CTV advertising: design models and validation, analyze large identity datasets with distributed tools, productionize data-science workflows, partner with engineering/product, and apply privacy-by-design while leveraging LLMs and AI-assisted development.

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

The role involves developing NLP systems for medical document analysis using advanced deep learning techniques, collaborating with domain experts, and deploying models in a healthcare setting.

Design and validate clinical and biomarker data pipelines, perform source-to-target mappings (e.g., EHR to data model), extend ontologies and vocabularies, ensure data quality, evaluate informatics tools, and support data architecture and analysis workflows for a regulatory-grade platform.

The Senior Data Scientist will analyze customer data, design experiments, build models, and support data-driven decision-making across product, marketing, and customer experience teams.

Develop, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

As a Pricing Data Scientist, you'll develop and validate pricing models for alternative assets, collaborate with engineers, and enhance valuation accuracy through data insights.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Lead experimentation and model development for NLP, LLM, and generative AI solutions in legal products. Design RAG, prompt strategies, agentic workflows, hybrid retrieval (semantic+lexical), and evaluation metrics. Analyze large-scale legal datasets, establish model validation and benchmarking best practices, translate results into product recommendations, collaborate with product/legal/engineering teams, and mentor data science team members.

Lead design, experimentation, and validation of agentic AI capabilities for legal products using ML, NLP, LLMs, RAG, and hybrid retrieval. Define agentic workflows, retrieval strategies, evaluation metrics, and benchmarking. Analyze large legal datasets, translate results into product recommendations, collaborate with product/engineering/legal experts, and mentor the data science team.

Lead design and validation of AI-driven legal product capabilities: develop NLP/LLM and generative AI solutions (RAG, prompts), design agentic workflows, build retrieval/hybrid search strategies with embeddings and vector DBs, analyze large legal datasets, establish model evaluation best practices, translate results into product recommendations, collaborate with product and engineering, and mentor junior team members.

Lead a team of data scientists to design, develop, and deploy advanced AI/ML solutions (especially LLMs/Transformers). Analyze large datasets, set team strategy, mentor juniors, define best practices, collaborate with cross-functional stakeholders, and ensure models align with business needs and governance.

Lead design and validation of agentic AI capabilities for legal workflows using LLMs, NLP, RAG, hybrid search, embeddings and vector databases. Drive experimentation, evaluation, and metrics, translate results to product recommendations, collaborate with engineers and legal experts, and mentor team members.

Lead AI and machine learning projects, mentoring junior data scientists, analyzing large datasets, and collaborating with cross-functional teams to align solutions with business goals.

Senior data analyst/programmer using SAS or R to analyze commercial claims and EHR real-world data. Create longitudinal cohorts, derive baseline/outcome variables, assess data quality, apply epidemiologic study designs and statistical methods (regression, survival), review protocols/SAPs, and program TFLs. Familiarity with OMOP CDM and OHDSI tools is a plus; prior pharmaceutical experience preferred.

The Senior Data Scientist will develop machine learning systems for fraud prevention using behavioral data, collaborate across teams, and mentor junior scientists.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

Lead end-to-end client ML engagements: scope problems, design, build, and deploy production ML solutions across cloud and containerized stacks. Lead and mentor small teams, manage client relationships, evaluate emerging tools, and ensure models are production-ready and explainable to technical and non-technical stakeholders.

Design, build, and deploy ML models across diverse use cases; work with managed cloud ML platforms and containerized solutions; collaborate with engineers on production integration and MLOps best practices; preprocess and analyze large datasets; mentor teammates and influence technical direction; translate complex ML concepts for technical and non-technical stakeholders.

Design, develop, and deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

The role involves building and validating Marketing Mix Models and geo-experiments, managing measurement infrastructure, and communicating insights to stakeholders.

Design, develop, and deploy time-series and machine-learning forecasting models (ARIMA, SARIMA, ETS, XGBoost, LightGBM) for demand planning, inventory, and sales forecasting. Perform large-scale SQL data processing, operationalize models in cloud environments (Azure preferred), present insights, and lead client workshops.