Hybrid Data Scientist

at S&P Global

• Job title: Data Scientist
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, AWS, Azure, Docker, DynamoDB, Kubernetes, MongoDB, NoSQL, Postgres, Python, Redis, SQL
• Location requirements: Hybrid, Mexico City, Mexico

About the role

• Data Scientist developing data extraction and machine learning pipelines for S&P Global. Building enterprise-scale data solutions and collaborating in a global team environment.

Responsibilities

• Develop, deploy, and operate data extraction and automation pipelines in production
• Integrate and deploy machine learning models into those pipelines (e.g., inference services, batch scoring)
• Lead critical stages of the data engineering lifecycle, including: End-to-end delivery of complex extraction, transformation, and ML deployment projects
• Scaling and replicating pipelines on AWS (EKS, ECS, Lambda, S3, RDS)
• Designing and managing DataOps processes, including Celery/Redis task queues and Airflow orchestration
• Implementing robust CI/CD pipelines on Azure DevOps (build, test, deployment, rollback)
• Writing and maintaining comprehensive unit, integration, and end-to-end tests (pytest, coverage)
• Strengthen data quality, reliability, and observability through logging, metrics, and automated alerts
• Define and evolve platform standards and best practices for code, testing, and deployment
• Document architecture, processes, and runbooks to ensure reproducibility and smooth hand-offs
• Partner closely with data scientists, ML engineers, and product teams to align on requirements, SLAs, and delivery timelines

Requirements

• Expert proficiency in Python, including building extraction libraries and RESTful APIs
• Hands-on experience with task queues and orchestration: Celery, Redis, Airflow
• Strong AWS expertise: EKS/ECS, Lambda, S3, RDS/DynamoDB, IAM, CloudWatch
• Containerization and orchestration: Docker (mandatory), basic Kubernetes (preferred)
• Proven experience deploying ML models to production (e.g., SageMaker, ECS, Lambda endpoints)
• Proficient in writing tests (unit, integration, load) and enforcing high coverage
• Solid understanding of CI/CD practices and hands-on experience with Azure DevOps pipelines
• Familiarity with SQL and NoSQL stores for extracted data (e.g., PostgreSQL, MongoDB)
• Strong debugging, performance tuning, and automation skills
• Openness to evaluate and adopt emerging tools and languages as needed
• Bachelor's degree in Computer Science, Engineering, or related field (Good to have)
• 2-4 years of relevant experience in data engineering, automation, or ML deployment (Good to have)
• Prior contributions on GitHub, technical blogs, or open-source projects (Good to have)
• Basic familiarity with GenAI model integration (calling LLM or embedding APIs) (Good to have)

Benefits

• Health & Wellness: Health care coverage designed for the mind and body.
• Flexible Downtime: Generous time off helps keep you energized for your time on.
• Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
• Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
• Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
• Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.