Top Data Science Jobs in Hampton, VA

Apply advanced analytics, statistical modeling, and machine learning to build and operationalize predictive models for pricing, underwriting, claims, and operations. Partner with business, product, and engineering teams to integrate models, monitor performance, improve data pipelines, and deliver dashboards and insights that inform strategic decisions.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and deploy NLP solutions: perform EDA on text, design and evaluate models (classification, NER, information extraction), develop pipelines from regex to transformers, apply generative AI and prompt engineering, write production-grade Python, and collaborate with ML engineering to deploy and monitor scalable cloud-based solutions.

Develop and deploy statistical and machine learning models to analyze customer behavior across marketing and digital channels. Design and evaluate experiments/A-B tests, apply causal inference, build scalable Python/SQL pipelines, query Redshift, use SageMaker for production models, maintain version-controlled workflows, and present insights to cross-functional stakeholders.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Design, build, and deploy machine learning and generative AI models (including LLMs and text-to-image). Analyze large datasets, run A/B and statistical tests, collaborate with data engineering and engineering teams, and create dashboards/visualizations to communicate insights. Optimize data pipelines and model performance to support data-driven decisions.

Turn multi-model signals into measurement, forecasts, and optimizations. Clean and label large datasets, extract structure with LLMs/NLP, define product-grade metrics, run experiments, build predictive models, create evals and monitors, and translate insights into product and business decisions.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, build, and deploy production-ready generative AI applications: architect multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deliver scalable FastAPI-based services with monitoring, CI/CD, and containerized deployment.

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Design, develop, and deploy ML, NLP, and generative AI solutions to improve scientific discovery. Build retrieval, recommendation, ranking, and RAG systems; fine-tune and evaluate LLMs; create evaluation frameworks; build scalable data pipelines; collaborate with cross-functional teams and produce production-quality Python code to deliver reliable, impactful AI systems for research use.

The Data Scientist will lead advanced analytics and drive decision-making through data engineering, modeling, and visualization, primarily supporting the Air Force's Intelligence mission.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

The Data Scientist will work on data analysis, visualization, and reporting, utilizing various tools to assist stakeholders' needs.

The Data Scientist II will drive insights, develop models, and analyze product experiments to inform business decisions and improve member experiences in healthcare.

Designs, analyzes, and interprets clinical trial, RWE, and observational data for a medical device. Develops statistical plans, predictive models, and ensures data quality, regulatory compliance, and traceability. Collaborates with clinical, regulatory, and data teams, supports submissions and publications, mentors interns, and represents the company externally.

Build and productionize machine learning models for healthcare products, define and track KPIs, collaborate with product and customers to scope analytics projects, communicate results to technical and non-technical stakeholders, and improve analytics practices across the team.

Build and operationalize machine learning and AI solutions for hospitality pricing, forecasting, and revenue management. Analyze large datasets, write Python and SQL to prepare and validate data, and partner with product and engineering to turn analyses into product capabilities. Apply modern AI techniques, including LLMs and AI coding tools, and communicate insights to stakeholders.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

As a Data Scientist at HOPPR, you'll develop and implement data pipelines and MLOps practices, utilizing large datasets and collaborating across teams to enhance AI applications in healthcare.