Grade Level (for internal use):

05

Role::  Overview This is an Apprentice role for college graduates for an 12-month fixed program .

Join our dynamic Modeling & Science team at S&P Global as a Data Scientist Apprentice. This role is perfect for recent graduates eager to apply their analytical skills and learn in a collaborative environment. You will work on the development of data solutions for both internal and external stakeholders.

Key Responsibilities:
Assist in data collection, cleaning, and preprocessing.
Support the team in developing models and proof of concepts.

Research on latest AI/ML technologies.
Contribute to data visualization and reporting efforts.
Participate in team meetings and brainstorming sessions.

Qualifications:
Bachelor’s degree in Data Science, Computer Science, Statistics, or related field.
Strong familiarity with Python programming language.
Basic understanding of machine learning concepts and common Frameworks
Strong analytical and problem-solving skills.

Preferred Skills:
Internship or project experience in data science.
Eagerness to learn and adapt quickly.
Excellent communication skills.

Knowledge on the commodity market.

About S&P Global Commodity Insights
At S&P Global Commodity Insights, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value.

We’re a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Commodity Insights’ coverage includes oil and gas, power, chemicals, metals, agriculture and shipping.

S&P Global Commodity Insights is a division of S&P Global (NYSE: SPGI). S&P Global is the world’s foremost provider of credit ratings, benchmarks, analytics and workflow solutions in the global capital, commodity and automotive markets. With every one of our offerings, we help many of the world’s leading organizations navigate the economic landscape so they can plan for tomorrow, today.