Agricultural Valuation & Machine Learning Modell SME (Contract)

Role Purpose

The Agricultural Valuation & Machine Learning Model SME will provide specialist advisory, research, and knowledge management services to support the development, validation, and interpretation of machine learning-driven agricultural valuation models. The role bridges agricultural valuation expertise, business requirements, data science, and credit decisioning to ensure valuation approaches are practical, credible, and aligned to business objectives.

Key Responsibilities

• Provide expert guidance on agricultural valuation methodologies and asset pricing principles.

• Advise on the development and refinement of machine learning valuation models.

• Support identification of valuation drivers, assumptions, and business rules.

• Review and interpret model outputs and provide recommendations.

• Conduct research on valuation practices, market trends, and benchmark data.

• Facilitate knowledge transfer sessions and stakeholder workshops.

• Translate technical modelling concepts into business language.

• Engage with Product, Credit Risk, Agriculture Banking, Data Science, Technology, and Project teams.

• Produce executive presentations, research papers, and recommendation reports.

• Support governance, model review, and decision-making forums.

Required Skills and Experience

• Demonstrated experience in agricultural valuation, agribusiness economics, agricultural finance, or related disciplines.

• Strong understanding of valuation methodologies and collateral valuation principles.

• Experience supporting analytical, statistical, or machine learning models.

• Strong research, analytical, and documentation skills.

• Ability to communicate effectively with business, technical, and executive stakeholders.

• Experience within banking, agricultural finance, credit risk, or lending environments is advantageous.

Key Deliverables

• Agricultural valuation methodology recommendations.

• Model review, validation, and advisory feedback.

• Data and valuation research reports.

• Stakeholder education and knowledge transfer material.

• Executive briefing papers and presentation packs.

• Valuation framework documentation.

• Inputs into POC recommendations and implementation planning.

Success Measures

• Improved quality and credibility of valuation outputs.

• Increased stakeholder confidence in valuation methodologies.

• Effective translation of model outputs into business insights.

• High-quality advisory and knowledge management support.

• Timely delivery of agreed research and documentation

• Engage with Product, Credit Risk, Agriculture Banking, Data Science, Technology, and Project teams.

• Produce executive presentations, research papers, and recommendation reports.

• Support governance, model review, and decision-making forums.