Sustainability Analytics Lead (H/F) Nigeria


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Offre pourvue
Offre
Sustainability Analytics Lead (H/F)
Rémunération
A NEGOCIER
Publié le
04/07/2022
Contrat
Contrat permanent
Localisation
Nigeria
Ref
n°: JO-0149918

Fed Africa est un cabinet de recrutement par approche directe et par annonces, dédié au continent africain, intervenant sur des fonctions de Middle et Top Management, Fed Africa a pour vocation d'accompagner les groupes locaux et internationaux dans leurs développements sur la zone Afrique.

Nous recherchons pour un de nos clients, une multinationale dans le secteur de l'agroalimentaire, un Sustainability Analytics Lead (F/H) basé au Nigeria.

Votre fonction

* Lead the development of the environmental, social & Goverance (ESG) metrics, methodologies and tools.
* Apply AI and other quantitative methods & must also demonstrate strong quantitative skills, including AI and Machine learning methods
* Support the ESG pillar leads' needs in research, data analysis, anlytics lead solutions, and mathematical/statistical modeling.
* Build, validate and update complex data models.
* Monitor social and environmental risks in farms and supply chains, harnessing a variety of sources and tools.
* In collaboration with sustainability and digital colleagues, coordinate the development of digital solutions and applications.
* Stay abreast of trends and best practices in data mining and analytics.

Votre profil

* Master's or Ph.D. degree in data analytics, statistics, GIS systems, environmental anaytics, physics, mathematics, statistics, data science (preferred including Machine Learning), or equivalent quantitative research experience if she/he has a different academic background
* 5-10 years of relevant professional experience
* A personal passion for sustainable agriculture and food systems
* Experience managing data science and quantitative analysis within a data-intensive environment.
* 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
* On-the-ground experience and a good understanding of agri-business models and success factors are highly desireable