Senior Data Scientist - Commodity Trading And Risk Managemen

Consulting

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,