Data Scientist III

About the role

Grade Level (for internal use): 10 About the Role : 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. You'll work closely with colleagues from data science, engineering, and business teams to deliver scalable, reliable, and cloud-enabled data solutions. We welcome applicants from diverse backgrounds and encourage you to apply even if your experience doesn't match every requirement. 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. Your contributions will directly influence business decisions and product innovation. Collaboration & Growth: Work alongside talented colleagues from data science, engineering, and business teams. You'll have opportunities to learn, share knowledge, and grow your expertise in both data science and data engineering. Cloud & Modern Tech: Gain hands-on experience with leading cloud platforms (such as AWS) and modern data tools, helping you stay at the forefront of industry trends. Hybrid Flexibility: Enjoy a flexible work environment that supports both onsite collaboration and remote work, promoting work-life balance and productivity. Inclusive Culture: Be part of a diverse and inclusive team that values different perspectives and backgrounds. We support your success and well-being, including accommodations during the hiring process.

Responsibilities

• Collaborate with stakeholders (data scientists, analysts, engineers, business teams) 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.
• Experience with data governance practices (privacy, security, access controls, compliance expectations).
• Experience improving data observability (monitoring, alerting, SLAs, incident response).
• Why This Role Matters
• Your work will help teams across S&P Global use trusted, well-modeled, and well-delivered data to build analytics and ML capabilities-supporting product innovation and decision-making with robust data foundations.
• About S&P Global Energy
• At S&P Global Energy, our comprehensive view of global energy and commodities markets enables our customers to make superior decisions and create long-term, sustainable value. Our four core capabilities are: Platts for news and pricing

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