Job description

Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape. For over 50 years our work has guided the decisions of the world’s most influential energy producers, utilities companies, financial institutions and governments. Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s possible with Intelligence Connected. By fusing our unparalleled proprietary data with the sharpest analytical minds, all supercharged by Synoptic AI, we deliver a clear, interconnected view of the entire value chain. Our trusted team of 2,700 experts across 30 countries breaks siloes and connects industries, markets and regions across the globe. This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead. Wood Mackenzie Intelligence Connected WoodMac.com Wood Mackenzie Brand Video Wood Mackenzie Values Inclusive – we succeed together Trusting – we choose to trust each other Customer committed – we put customers at the heart of our decisions Future Focused – we accelerate change Curious – we turn knowledge into action About the role Wood Mackenzie s trading customers act on our forecasts and analytical models every day, across power, gas and oil markets. We are turning that modelling estate into a portfolio of products in its own right: versioned, documented, commercially packaged, and consumable by machines as readily as by analysts. The Director of Modelling Products owns that portfolio. You set the strategy for what we model and why, you own the commercial case for investment across commodities, and you lead the product leads who hold each market. You will not build the models yourself and you will not own the engineers who implement them. You decide what the portfolio should contain, hold the standard it is judged against, and make the case for what we invest in next. What you will own The portfolio strategy. Which models we build, rebuild, retire or acquire across power, gas and oils. You own the investment case and the sequencing, and you defend both. Productization of analytical assets. Moving models and their underlying features from research outputs into products, with versioning, documented methodology, reproducible outputs and a defined release cycle. This is the central program of the role. The modelling product leads. Senior product people holding market specific depth in their commodities. You hire them, develop them, set their objectives, and hold a consistent methodological standard across them. Methodology governance. One standard for validation, change control, backtesting and documentation, so that a colleague or a customer can understand what a model does and how much to trust it. Commercial packaging and distribution. How modelling assets reach customers: in applications, through APIs and data platforms, and as inputs to our own analysts. You work with commercial teams on pricing and packaging. The partnership with central engineering and modelling functions. Development capacity is provided centrally and built to your specification. You set requirements, review implementations and hold the quality bar without managing the developers. What we are looking for A quantitative background you can draw on. A degree in a mathematical, engineering or scientific discipline, and real experience with linear programming, econometric or machine learning modelling. You need to be able to discuss, validate and challenge a methodology on its merits rather than take it on trust. Portfolio level product leadership in analytics or data. Ten years or more in product, including ownership of a multi-product portfolio and the investment tradeoffs across it. Experience leading product managers, including senior ones. Your leverage in this role comes through other people. A track record of productizing analytical or research assets. Taking work built for internal or bespoke use and turning it into something repeatable, documented and commercially viable. Machine consumable delivery. APIs, streaming, data platforms and workflow integration. Our customers increasingly want the model and the data rather than the report. Commodity or energy market context preferred. Deep subject matter expertise is not a prerequisite, since market specific depth sits with peers or the leads reporting to you. You do need enough market and commercial literacy to prioritize well. Comfort operating without direct control. Development, data engineering and market expertise all sit with teams you do not manage. Clear specifications, credibility and influence are how this role works. What this role is not Not a modelling role. You will not be building or running models. You direct, prioritize and hold the standard. Not an engineering or data science management role. Those capabilities are provided by central functions. Not a research leadership role. Our analyst teams produce market views and commentary. You own the models they and our customers depend on. Not a finished portfolio. The scope will expand across commodities as the function develops. The salary range for this position is $160,000- $190,000, which represents base pay only and does not include short-term incentive compensation or commission. When determining base pay, as part of a final compensation package, we consider several factors such as location, experience, qualifications, and training. This base salary maps to the Boston Location. Equal Opportunities We are an equal opportunities employer. This means we are committed to recruiting the best people regardless of their race, colour, religion, age, sex (including pregnancy, sexual orientation, and gender identity) , national origin, disability or protected veteran status. You can find out more about your rights under the law at www.eeoc.gov

Requirements

Must-haves

Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape.
For over 50 years our work has guided the decisions of the world’s most influential energy producers, utilities companies, financial institutions and governments.
Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s possible with Intelligence Connected.
This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead.
Wood Mackenzie
Intelligence Connected
Wood Mackenzie Brand Video
Wood Mackenzie Values
Inclusive – we succeed together
Trusting – we choose to trust each other
Customer committed – we put customers at the heart of our decisions
Future Focused – we accelerate change
Curious – we turn knowledge into action
What you will own
The portfolio strategy. Which models we build, rebuild, retire or acquire across power, gas and oils. You own the investment case and the sequencing, and you defend both.
The modelling product leads. Senior product people holding market specific depth in their commodities. You hire them, develop them, set their objectives, and hold a consistent methodological standard across them.
Methodology governance. One standard for validation, change control, backtesting and documentation, so that a colleague or a customer can understand what a model does and how much to trust it.
Commercial packaging and distribution. How modelling assets reach customers: in applications, through APIs and data platforms, and as inputs to our own analysts. You work with commercial teams on pricing and packaging.
What we are looking for
Portfolio level product leadership in analytics or data. Ten years or more in product, including ownership of a multi-product portfolio and the investment tradeoffs across it.
Experience leading product managers, including senior ones. Your leverage in this role comes through other people.
A track record of productizing analytical or research assets. Taking work built for internal or bespoke use and turning it into something repeatable, documented and commercially viable.
Machine consumable delivery. APIs, streaming, data platforms and workflow integration. Our customers increasingly want the model and the data rather than the report.
Comfort operating without direct control. Development, data engineering and market expertise all sit with teams you do not manage. Clear specifications, credibility and influence are how this role works.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learning

Platform: workday
Effort: long