Top Senior Level Data Science Jobs in Wilmington, NC

The MySQL DBA will manage and optimize MySQL databases, ensuring performance, security, and availability. Responsibilities include installation, monitoring, backups, disaster recovery, and collaboration with developers for query optimization.

The Principal Data Scientist leads the development of data-driven solutions, employing advanced analytics and machine learning to derive actionable insights. This role involves managing complex projects, collaborating with cross-functional teams, and developing training materials to enhance data literacy across the company.

As a Staff Data Scientist at Zora, you will manage complex data challenges, design and build data systems like ETLs and dashboards, and promote a culture of experimentation through A/B testing. You will also develop metrics for products and contribute to algorithms and recommendations, collaborating with stakeholders to drive data-led decisions.

As a Senior Gen AI Data Scientist, you will join the Generative AI team, focusing on LLM-based enterprise solutions. Responsibilities include testing and deploying generative AI models, collaborating with teams to define project objectives, enhancing model performance via prompt engineering, and developing evaluation pipelines. Staying current with research in Generative AI is also key, along with contributing to team upskilling efforts.

The Principal Data Scientist will lead the predictive analytics practice, focusing on deploying machine learning models for financial metrics and customer forecasts, collaborating across teams to enhance operational capabilities and model performance.

The Senior Software Engineer will lead or collaborate on the design, development, and deployment of software features, ensuring clean, reusable coding standards and effective problem-solving with clients. This role involves improving code architecture and maintaining high-quality documentation.

The Senior Data Scientist will develop and deploy lead scoring and recommendations models to increase sales call conversion rates, work with business stakeholders to prioritize data modeling projects, establish best practices for model performance evaluation, mentor team members on ML projects, and guide data modeling needs for machine learning systems.