Market Risk Analyst II

Job Description

Analyst, RMG Market Risk (AI & Quantitative Focus) | Cargill

The Tone:
This is a full-time role at Cargill. No specific location is mentioned in the listing. Cargill is focused on identifying, assessing, and mitigating risk within its trading organization. This role is crucial for applying quantitative analytics, artificial intelligence, and data-driven insights to manage potential losses and opportunities stemming from market movements, ensuring adherence to policy and enhancing decision-making.

The TL;DR
• Role: Full Time
• Type: Full-time
• Mission: Identify, assess, and mitigate market risk using advanced analytics and AI to enhance decision-making and manage financial consequences of market movements.
• Tech Stack: Python, SQL, R

What You’ll Actually Do
• Risk Assessment & Communication: Assess and communicate business risks, market trends, and analytical insights to functions across the organization.
• Model Development: Develop and implement controls, quantitative models, and data-driven approaches to minimize organizational risks and enhance commercial decision-making.
• Financial Consequence Estimation: Contribute to the estimation of potential financial consequences of market movements using statistical, quantitative, and predictive analytics techniques.
• AI/ML Tooling: Support the development and application of AI, machine learning, and analytical tools to improve risk assessment, forecasting, and trading performance.
• Data Analysis: Analyze large and complex datasets to identify trends, patterns, and opportunities relevant to trading and risk management activities.

The Must-Haves
• Background: Bachelor’s degree in Quantitative Finance, Mathematics, Statistics, Data Science, Computer Science, Engineering, Economics, or a related discipline, or equivalent experience.
• Experience: Minimum 2 years of relevant experience, including working with data analysis tools such as Python, SQL, or R.
• Skills: Strong analytical, quantitative, and problem-solving skills, with the ability to communicate complex analytical findings to both technical and business stakeholders.
• Bonus: Experience in AI, machine learning, predictive analytics, or quantitative modeling; exposure to commodity markets, trading, risk management, or financial markets; or experience building analytical models, forecasting tools, or decision-support solutions.