-
ID
#51394245 -
Salary
TBD -
Source
McKinsey & Company -
Date
2024-04-03 -
Deadline
2024-06-02
Senior Data Scientist - Commodity Trading And Risk Managemen
Colorado, Denver, 80221 Denver USAConsulting
Senior Data Scientist - Commodity Trading and Risk Management
Who You'll Work With
As a member of the Commodity Analytics team, you will be based in one of the
following offices: Atlanta, Boston, Dallas, Denver, Houston,
Toronto, or Washington D.C You'll work closely with McKinsey's
Commodity Trading Service Line to support clients across sectors and
geographieCommodity 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, and 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.
What You'll DoAs 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 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.
QualificationsUndergraduate degree is 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 experien e
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 methodologyFOR U.S. APPLICANTS: McKinsey & Company is an Equal
Opportunity/Affirmative Action employer.
All qualified applicants will receive consideration for employment without
regard to sex, gender
identity, sexual orientation, race, color, religion, national
origin, disability, protected Veteran
status, age, or any other characteristic protected by applicable law.
Certain US states require McKinsey & Company to include a reasonable
estimate of the salary range for this role.
A reasonable estimate of the range for new joiners for this role in the United
States is $176,000 - $186,800.
Actual salaries may vary and may be above or below the range based on various
factors, including,
but not limited to an individual's assigned office location, experience
and expertise. Certain roles are also eligible for bonuses,
subject to McKinseyis discretion and based on factors such as individual
and/or organizational performance.
Additionally, McKinsey offers a comprehensive benefits package,