Top Data Science Jobs in Raleigh, NC

Seeking a Senior Data Scientist to lead predictive analytics and machine learning initiatives. Responsibilities include building ML models, data management, and mentoring junior data scientists. Must have expertise in AWS and strong statistical knowledge.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

As a Principal Data Scientist, you'll design and deploy cutting-edge machine learning models to drive financial success for customers and guide technical teams.

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.

Build and deploy credit and fraud risk models using advanced ML/statistical methods. Partner with stakeholders, extract insights from large transaction datasets, automate feature/production pipelines, mentor junior data scientists, and ensure model quality and deployment scalability.

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.

The Senior Data Scientist will integrate data from various sources, analyze web performance, and provide insights for strategic decision-making. Responsibilities include developing automated systems for content coverage, tracking metrics, and designing experiments to assess new initiatives' impact.

The Principal Data Scientist will lead analytics for Mortgage sales, focusing on customer retention and lead optimization. Responsibilities include applying advanced analytics to enhance marketing strategies, conduct customer segmentation, and oversee lead generation processes. This role requires strong programming skills and collaboration with business partners to communicate insights effectively.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

The Senior Data Scientist oversees data collection and preparation, designs study protocols, leads report development, trains junior employees, and manages project workstreams while collaborating with team members to optimize business performance.

Design, build, and deploy end-to-end production ML systems for audience targeting, lookalike generation, and propensity scoring. Own ML lifecycle from experimentation to production monitoring, leverage distributed/cloud platforms, implement vector similarity and embedding systems, engineer scalable features, and collaborate with engineering and product teams to ensure model quality and operational reliability.

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

Lead Data Scientist responsible for creating ML-driven risk assessments and AI workflows within a SaaS security platform, ensuring effective decision-making and system reliability.

As a Senior GTM Data Scientist, you will develop predictive models, analyze experiments, and provide actionable insights to GTM teams.

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.

As a Lead Data Scientist, you will shape data strategy, develop predictive models, analyze healthcare datasets, and collaborate with teams to enhance MDCalc's clinical decision tools.

As a Data Scientist, you'll empower the company with data insights, develop metrics and experiments, and collaborate with various teams to drive product decisions.

The Senior Data Scientist will lead analytics efforts, develop data models, and improve data pipelines to support Thyme Care's mission in oncology care, while collaborating with stakeholders to enhance performance metrics and reporting.

The Senior Data Scientist will analyze data to generate insights, manage data-driven projects, and collaborate with teams to inform business decisions.

The Lead Data Scientist will build, deploy, and improve machine learning models for a rental marketplace, managing projects from inception to launch and working collaboratively with cross-functional teams.

Design, build, and deploy end-to-end data science solutions for pricing, underwriting, claims, and operations. Perform analyses on structured and unstructured data, develop and monitor ML models, create dashboards and visualizations, implement data pipelines, and collaborate cross-functionally to prioritize analytics initiatives and communicate results to technical and non-technical stakeholders.

As Lead Data Scientist, you will develop machine learning and generative AI solutions for financial planning and strategic decision-making, mentor team members, and engage with finance leadership.

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.

The Senior Data Scientist will partner with multiple departments to deliver impactful data solutions, champion data adoption, and evolve data science standards.

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.