EDHECinfra provides market indices, benchmarks, and valuation analytics for unlisted infrastructure equity and private debt investors. Asset owners, asset managers, consultants, and asset valuation professionals use our datasets, which track thousands of investments in 25 countries over the past 20 years.
In 2021, EDHECinfra launched a new, ambitious new project: The Green Infrastructure Project. Its goal is "to create a commercially viable ESG data product for the use of investors in infrastructure."
The Green Infrastructure Project aims to develop science-based "sustainability risk" and "sustainability impact" exposure metrics for "infrastructure assets" across all sectors globally. These metrics will allow clients to report on and benchmark infrastructure investments in terms of ESG risks and impacts.
About the role:
We are looking for a data scientist who is passionate about sustainability and ESG. The job's primary purpose is to develop ESG exposure metrics for infrastructure assets applying AI and other quantitative methods.
The ideal candidate must also demonstrate strong quantitative skills, be intellectually curious, and meet deadlines independently.
The ideal candidate will also be creative, comfortable working in a multidisciplinary environment, and have a basic understanding of the sustainability and ESG landscapes (e.g., regulations, climate impact measurement, physical risk, and ESG-related estimations, etc.).
The candidate will report to the Green Infrastructure Project Director.
To contribute to the research component of the Green Infrastructure project with applied research, AI solutions, and quantitative methods.
To develop ESG metrics using AI and Machine Learning methods.
To support the team's needs in research, data analysis, AI solutions, and mathematical/statistical modeling
Build, validate and update complex data models.
Manage and test machine learning methods, processes, and techniques
To contribute and participate in the internal discussions about ESG, infrastructure, metrics, AI solutions, etc.
The candidate must have a master's or Ph.D. degree in physics, mathematics, statistics, data science (including Machine Learning), or equivalent quantitative research experience if she/he has a different academic background
At least six years working experience in related fields
Experience managing data science and quantitative analysis within a data-intensive environment. (e.g., energy, tech, finance desirable)
Demonstrated ability to work with large volumes of data
Excellent understanding and ongoing learning of Data Science and Machine Learning frameworks, tools, and algorithms, both for regression and classification problems (e.g., Python, ArcGIS, Java, MatLab are highly desirable)
Excellent understanding of statistics and statistical methods
Fluent English, and demonstrated written and spoken English capacity
Data-driven and detail-oriented mindset
Strong work ethic and sense of ownership
Additional desired qualities and experience
Experience in one or more of the following fields is an asset: infrastructure sector (e.g., energy, transport sectors), ESG, sustainable development, sustainable finance, climate change
Experience working in carbon emissions analysis
Experience working with spatial data and GIS is highly desirable
Familiarity with the GHG protocol and SFDR regulation