As a member of the Commodity Analytics team, you will be based in one of the following offices: Brussels, Amsterdam, Madrid or Lisbon .
You'll work closely with McKinsey's Commodity Trading Service Line to support clients across sectors and geographies.
Commodity Analytics helps commodity producers, processors, buyers, and traders across agriculture/softs, metals, energy, and consumer sectors improve commodity price risk capabilities with cutting-edge data science.
The Commodity Trading Service Line at McKinsey supports clients in: Commodity trading and risk strategy Trading operations transformation Trading and risk digitization driven by deep trading experts with hands-on trading experience and advanced analytics assets.
Our Risk Practice supports clients in many different industries facing challenges of developing and implementing tailored concepts for risk.
As a member of client service teams, you will leverage your creativity and problem-solving skills to tackle clients' most pressing issues using an analytical lens, meeting client needs and communicating your work to executive audiences.
Client counterparts span a wide range of audiences and functions from treasury and risk professionals, marketing & sales teams, procurement category managers, to high-level stakeholders (e.g., CFO).
When working internally, you will build innovative algorithms and products (what we call "IP development") to best meet our most common client needs, such as: Building price forecasting models for commodities markets.
Brainstorming and developing new offers and solutions to support future clients.
Collaborating with our engineers to design new interfaces to deliver faster, more impactful insights to our clients.
In this role, your work on the team will primarily involve: Applying advanced analytics to enable better commodity risk management decisions.
Maintaining and expanding existing hedging strategies by re-training existing models through process-driven approaches.
Modifying and improving algorithm performance across market regimes by introducing new features, data sources, and modelling approaches.
Identifying opportunities for our clients to increase earnings potential and reduce downside risk by back testing various risk management strategies.
Co-building bespoke tools with client data science teams that tailor machine-learning algorithms to attain an optimal balance of earnings and volatility given clients' risk appetite and capital constraints.
Collaborating with and training cross-functional client teams to instill long-lasting capabilities and ensure new decision-making models are embraced by organizations.
As part of McKinsey, you will receive best-in-class training in structuring business problems and serving as a client adviser, with opportunities to work closely with and learn from senior commodity and risk practitioners, as well as industry players shaping the future of commodity markets and trading.
You will gain access to unparalleled career acceleration with significant ownership and responsibility from the get-go in a collaborative, diverse, non-hierarchical environment.
You will have the opportunity to travel to client sites locally and around the world (once travel resumes).
Lastly, you will be able to provide direct and measurable impact to some of the largest organizations in agribusiness, materials, energy, industrial, and consumer foods sectors around the globe.
Qualifications: Undergraduate degree required; advanced degree in a quantitative discipline such as computer science (especially machine learning), applied mathematics, economics, quantitative finance, or engineering is preferred or equivalent practitioner experience.
2+ years of commodity markets experience developing trading or hedging strategies (especially physical/cash markets) or price-discovery analysis in basic materials/metals, agriculture, softs, chemicals, plastics, or oil & gas preferred.
Experience writing clean, efficient Python code involving model development and deployment using state-of-the-art tools and libraries (e.g.
scikit-learn, pandas, etc.).
Experience applying advanced analytical and statistical methods to solve business problems involving commodity markets.
Ability to explain nuances of commodity markets and complex analytical concepts to people from other fields.
Experience working with version control (e.g.
Git), shell scripting, and Agile methodology.
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