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Quantitative Analyst - Commodities

McKinsey & Company
Quantitative Analyst - Commodities
Denver, CO
171,000 - 276,000
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Job Description


You will leverage market knowledge and analytical expertise to provide insights both to clients as part of client service teams and within our team by strengthening the core products and algorithms we build for clients. You can expect to split your time delivering impact at clients and building up ACRE’s core analytical offerings in our Denver, Houston, or Waltham office.

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, from building price forecasting models for commodities markets, to brainstorming and developing new offers and solutions to support future clients. You will also work 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 be in applying advanced analytics to enable better commodity and FX risk management decisions. For example, you might work as the lead in maintaining and expanding existing hedging strategies by re-training existing models through process-driven approaches. You might also modify and improve algorithm performance across market regimes, by introducing new features, data sources, and modelling approaches; rapidly identify opportunities for our clients to increase earnings potential and reduce downside risk by back testing various risk management strategies; co-build 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; and/or collaborate with and train 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 and have opportunities to work closely with and learn from our senior commodity and risk practitioners, as well as industry players that are shaping the future of commodity markets and trading. You will get access to unparalleled career acceleration, with a huge amount of ownership and responsibility from the get-go in a collaborative, diverse, non-hierarchical environment. You will get 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.

We encourage you to submit a concise cover letter about why you believe that you are a unique fit for our team and what we do! We will read it.


  • Undergraduate degree; MSc or PhD level degree in a quantitative discipline such as computer science (especially machine learning), applied mathematics, behavioral economics, quantitative finance or industrial engineering or equivalent practitioner experience
  • 3+ 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 ETL processes, data manipulation, and standard data science packages (e.g., SciPy, NumPy, Pandas, SKlearn)
  • 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 creating and implementing machine-learning models and dealing with large data sets (e.g., time series/econometrics models, linear models with regularization algorithm, classification algorithms like random forest, support vector machine, or LGBM)
  • Experience working with production level IDEs (e.g., Visual Studio, PyCharm), interactive IDEs (e.g., Spyder, Jupyter), and Version Control(e.g., git, svn)
  • Creative, naturally curious and willing to take intellectual risks
  • Comfortable working under competing, quickly changing priorities
  • Willingness to travel up to 50%
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