Data Scientist

Data Scientist- Hybrid, Cary, North Carolina

 

We’re a leader in data and AI. Through our software and services, we inspire customers around the world to transform data into intelligence - and questions into answers.

 

If you're looking for a dynamic, fulfilling career with flexibility and a world-class employee experience, you'll find it here. We're recognized around the world for our inclusive, meaningful culture and innovative technologies by organizations like Fast Company, Forbes, Newsweek and more.

 

About the job

The Applied AI & Modeling (AAIM) team is looking for a Data Scientist to help advance a multi‑phase applied research and development effort focused on computer vision and machine learning for high‑impact real‑world data. Our team works at the intersection of advanced modeling, scalable AI systems, and domain‑driven problem solving, partnering closely with engineers and subject‑matter experts to turn emerging research into practical, measurable outcomes.

 

This is an exciting opportunity to build and evaluate state‑of‑the‑art vision models while working on problems that demand both technical depth and rigor. You will contribute to the evolution of existing prototypes into more robust, scalable solutions, gaining hands‑on experience with 3D vision, attention mechanisms, and modern deep learning architectures in a collaborative environment. This role is well‑suited for someone who wants to grow as an applied data scientist, learn how advanced AI systems are developed responsibly, and see their work directly influence the next stage of real‑world AI innovation.

 

As a Data Scientist, you will:   

• Design, develop, and evaluate machine learning and computer vision models to solve complex, real‑world problems using large and diverse datasets.
• Apply and extend modern deep learning techniques (e.g., convolutional models, 3D vision, attention mechanisms, transformer‑based approaches) to improve model accuracy, robustness, and scalability.
• Analyze model performance using appropriate quantitative metrics, identify failure modes, and iterate on solutions based on experimental findings.
• Collaborate with fellow data scientists, software engineers, and cross‑functional partners to integrate models into end‑to‑end analytical workflows.
• Contribute to well‑documented, reproducible modeling pipelines and clearly communicate insights, tradeoffs, and results to technical and non‑technical audiences.
• Ensure all applicable security policies and development processes are followed to support the organization’s secure and responsible software development goals.
• Embrace curiosity, passion, authenticity, and accountability - our values that guide how we work, learn, and innovate together.

Required qualifications 

 

• Master’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field with a minimum of 3 years of relevant professional experience; or PhD degree in a related quantitative field, with no prior professional work experience required.
• Demonstrated experience applying computer vision and machine learning techniques to real‑world problems, including tasks such as image analysis, feature extraction, model training, and performance evaluation.
• Hands‑on experience with at least one modern deep learning or computer vision framework (e.g., Python‑based frameworks commonly used for CNN‑ or vision‑based modeling).
• Experience working with real‑world datasets, including data preparation, model evaluation using quantitative metrics, and result interpretation.
• Ability to analyze results, troubleshoot models, and clearly communicate technical findings to both technical and non‑technical audiences.
• An equivalent combination of related education, training, and experience may be considered in place of the above qualifications.

 

Additional competencies, knowledge and skills

 

Key competencies

• Analytical Thinking – Ability to break down complex, ambiguous problems into structured analytical tasks, evaluate alternative approaches, and use data to support sound technical decisions.
• Collaboration – Ability to work effectively with cross‑functional partners, including data scientists, engineers, and domain experts, contributing constructively in a team‑based environment.
• Learning Agility – Willingness and ability to quickly learn new methods, tools, and domains, and apply new knowledge to evolving technical challenges.

 

Additional skills and experience (nice to have)