Trading Data Analytics Intern: Build Pipelines & Insights

XPS Materials Group; Singapore; On-site; SGD 20,000 - 31,000; Full time

Job summary

Glencore is seeking a Data Analytics Intern — Trading to collaborate with analysts, quants, and data engineers on real business needs. You will extract and clean data from APIs and internal sources, build Python/SQL pipelines, and create dashboards the trading teams rely on.

This hands-on role requires coding in Python/SQL, applying AI tools, and delivering actionable insights. Opportunities exist to advance into quant/engineering tasks with backtesting and production pipelines.

Qualifications

• Pursuing a STEM, quantitative, analytical, or business degree (e.g., Engineering, CS, Data Science, Mathematics, Statistics, Finance, Economics).
• Ability to code in Python and/or SQL from data cleaning upward or quick learner.
• Strong analytical, problem-solving, and attention-to-detail skills.
• Ability to translate business requirements into working results.

Responsibilities

• Extract and clean data from APIs, web sources, portals, and internal databases.
• Write Python/SQL to build pipelines, scrapers, and tools for the trading desk.
• Apply AI tools to automate tasks and improve analyses.
• Prepare charts, dashboards, and summaries for the desk.
• Track vessel movements, cargo flows, refinery events, and supply/demand indicators.
• Investigate data-quality issues.
• Advanced scope: develop statistical/ML models and backtesting for trading workflows.

Skills

Python

SQL

Data cleaning

APIs

LLMs/AI tools

Pandas/NumPy

Education

STEM/Quant degree (pursuing)

Tools

Python data libraries

Databricks/AWS/Azure

Job description

Glencore is seeking a Data Analytics Intern — Trading to collaborate with analysts, quants, and data engineers on real business needs. You will extract and clean data from APIs and internal sources, build Python/SQL pipelines, and create dashboards the trading teams rely on.

This hands-on role requires coding in Python/SQL, applying AI tools, and delivering actionable insights. Opportunities exist to advance into quant/engineering tasks with backtesting and production pipelines.