Data Science Analyst - ACRE, Commodities

Key Skills

Java Programming, Python Programming, SQL, Visualforce, Data science techniques, Jupyter Notebook, NumPy, Pandas, SciPy, RStudio, ETL frameworks, PyCharm

Job Description

• As a member of the CommodityFX Analytics team, you’ll work with McKinsey’s agricultural analytics team (ACRE), McKinsey’s Risk Practice, and client service teams that support clients across sectors and geographies. 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.
• CommodityFX Analytics helps commodity consumers and producers across agriculture, basic materials, and energy sectors identify, measure and optimize their price risk exposure to commodity and FX markets with data science.
• Our Risk Practice supports clients in many different industries facing challenges of developing and implementing tailored concepts for risk recognition, measurement, and control. Facing extreme volatility in financial and commodity markets, more and more of our clients are realizing that effective, risk-informed strategy can offer a major source of competitive advantage.
• Our Agriculture Practice advises agribusiness, consumer food, government, and investor clients on strategic, marketing & sales, and operations issues, helping support industry-shaping decisions that impact the future of global food production.
• ACRE is a team of ~45 expert consultants, data scientists, and engineers focused on bringing cutting-edge analytics to agriculture & related clients. ACRE is an agile team within the firm whose goal is to use the latest analytical methods, incubate new technologies and drive innovative ways to develop new opportunities for the firm to make significant and lasting client impact – redefining what it means to provide the “best of the firm” to our clients.

What You'll Do

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.

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.

Qualifications

• Undergraduate degree; Masters 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
• 1+ years of commodity markets experience developing trading or hedging strategies (especially physical/cash markets) or price-discovery analysis in basic materials, agriculture, 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%