Data Scientist - Agriculture Commodity Research Engine (ACRE), New Ventures

Qualifications

  • Undergraduate degree in agriculture and/or a quantitative discipline
  • Degree examples include economics, mathematics, computer science, geography, statistics, data science, plant biology, crop science, agronomy, environmental science
  • M.B.A. or other professional degree is beneficial
  • Self-management skills and ability to work as part of an Agile team
  • Strong multitasking and parallel development abilities
  • Passion for problem solving on a global scale
  • Strong interpersonal communication skills
  • Creative and willing to take intellectual risks
  • Experience with trading or price-discovery a plus
  • Experience creating and implementing machine-learning models a plus
  • Experience with Python and/or R is a plus

Who You'll Work With

You'll work with our Agriculture Commodity Research Engine (ACRE) team in Denver. ACRE is part of McKinsey's New Ventures.

The ACRE team is part of the Agriculture Practice and it applies advanced analytics and big data techniques to global agricultural markets, driving insights at the micro and macro levels. Our Agriculture Practice advises agribusiness, consumer food, government, and investor clients on strategic and operations issues, helping support industry-shaping decisions that impact the future of global food production.

McKinsey New Ventures fosters innovation driven by analytics, design thinking, mobile and social by developing new products/services and integrating them into our client work. It is helping to shift our model toward asset-based consulting and is a foundation for – and expands our investment in – our entrepreneurial culture. Through innovative software as a service solutions, strategic acquisitions, and a vibrant ecosystem of alliances, we are redefining what it means to work with McKinsey.

As one of the fastest-growing parts of our firm, New Ventures has more than 1,500 dedicated professionals (including more than 800 analysts and data scientists) and we're hiring more mathematicians, data scientists, designers, software engineers, product managers, client development managers and general managers.

What You'll Do

You will leverage industry knowledge and analytical expertise as a member of client engagement teams by providing insights both in front of clients and within the ACRE team.

You will deploy creative agricultural analytics in cloud computer settings. You will sharpen your programming and statistical abilities while quickly acquiring knowledge in a variety of topics of key import to our agribusiness clientele.

As a member of client engagement teams, you will leverage your creativity and problem solving skills to tackle clients' company-level issues. These issues could include predicting future market price trends, determining the value of new technologies, incorporating market and crop data to predict trade flows, or optimizing agricultural operations in developing economies. Using your quantitative and programming skills, you will develop advanced insight into these issues and learn to effectively communicate them to executive audiences.

Along the way, you will receive best-in-class training in structuring business problems and serving as a client adviser. You will be responsible for providing conceptual insights, problem structuring, modeling, and other analytic support to client engagement teams. In addition, you will help codify the knowledge that you build into interactive data tools, with the help of our data sciences teams, by designing new interfaces to deliver faster, more impactful insights to our clients related to the knowledge area that you will champion.

You will develop and continuously deepen your expertise in agriculture through this role, specializing in agriculture and developing your problem-solving skills as well as teamwork and leadership skills. You will have opportunities to work closely with and learn from our senior agriculture practitioners, and work directly with industry players that are shaping the future of food production.


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