Data Scientist III

S&P Global is seeking a Data Scientist III (Grade 10) to join our Data Science & Modeling team. In this hybrid role, you will help build and optimize the data infrastructure that powers analytics and machine learning across the organization.

Responsibilities:

• Collaborate with stakeholders to understand data needs and translate them into actionable technical requirements.
• Design, build, and maintain scalable data pipelines using modern tools and cloud platforms (such as AWS, Azure, or GCP).
• Develop and maintain data infrastructure for analytics and machine learning, leveraging cloud-native technologies and services.
• Improve data quality, lineage, and reliability, and troubleshoot issues across the data lifecycle.
• Support the deployment and monitoring of analytics and ML solutions in production, working closely with data science and engineering peers.
• Contribute to an Agile team environment, including documentation, code reviews, and iterative delivery.

Requirements:

• University degree in Computer Science, Engineering, Mathematics, or a related discipline (or equivalent practical experience).
• 3+ years of hands-on programming experience, primarily in Python, with a strong focus on building, training, and evaluating machine learning and deep learning models.
• Strong experience working with machine learning frameworks and libraries (e.g., PyTorch, TensorFlow, Keras, scikit-learn), including designing and implementing neural network architectures.
• Proven experience in computer vision techniques and applications, such as image/video processing, feature extraction, object detection, segmentation, and representation learning.
• Experience developing end-to-end ML workflows, from data preparation and feature engineering through model training, validation, and deployment, with an emphasis on model performance, robustness, and maintainability.
• Working knowledge of data storage and querying technologies (SQL and/or NoSQL) to support analytical and ML workflows.
• Hands-on experience using cloud platforms (such as AWS, Azure, or GCP) for machine learning experimentation, training at scale, and model deployment.
• Familiarity with distributed and accelerated computing concepts relevant to ML workloads (e.g., multi-GPU training, distributed training, or large-scale inference).
• Experience collaborating closely with data engineers and platform teams to integrate models into production pipelines, while maintaining ownership of model logic and performance.
• Exposure to workflow orchestration or experiment management tools (e.g., Airflow, MLflow, or similar) for reproducible ML pipelines is a plus.
• Familiarity with version control systems (e.g., Git) and best practices for collaborative ML development.
• Strong ability to communicate results, trade-offs, and limitations of ML models clearly to both technical and non-technical stakeholders.

What's in for you:

• Impactful Work: You'll play a key role in shaping the data infrastructure that powers advanced analytics and machine learning solutions across S&P Global.
• Collaboration & Growth: Work alongside talented colleagues from data science, engineering, and business teams.
• Cloud & Modern Tech: Gain hands-on experience with leading cloud platforms (such as AWS) and modern data tools.
• Hybrid Flexibility: Enjoy a flexible work environment that supports both onsite collaboration and remote work.
• Inclusive Culture: Be part of a diverse and inclusive team that values different perspectives and backgrounds.