Short-Term Consultant -Agricultural EO/ML Expert

Application Closing Date: April/22/2026

To Submit Your Application:

Interested candidates should send their CV and a letter of interest to Kichan Kim (kkim11@worldbank.org) and Jimin Shim (jshim2@worldbank.org). Please ensure that both email addresses are included as recipients. The subject line of the email should be "STC Application_EO/ML_[Your Name]". Only shortlisted candidates will be contacted for an interview.

Description of the Project:

Agriculture and Food GP global knowledge report on AI-enabled digital measurement, reporting, and verification (dMRV) for sustainable land management (SLM) incentive schemes in smallholder settings.

Background:

The Agriculture and Food Global Practice (AGF GP) supports countries to advance inclusive, resilient, and sustainable agrifood systems through policy reforms, investments, and knowledge. A growing share of this agenda depends on changing land-use and farm-level practices at scale—improving soil health, reducing land degradation, strengthening climate resilience, and sustaining productivity under increasing climate variability. Yet these reforms often hinge on delivery constraints: governments and implementing agencies must be able to identify eligible farmers and plots, track compliance or performance over time, and distribute benefits in ways that remain credible, timely, and administratively feasible in smallholder-dominated landscapes.

Recent advances in satellite-based Earth observation, digital soil information, cloud computing, and machine learning are changing what can be operationalized in routine delivery. The key shift is not that EO exists, but that AI-enabled processing and digital workflows increasingly make monitoring repeatable, automatable, and operational within policy timelines—supporting targeting, supervision, learning, and risk-based validation rather than after-the-fact reporting alone. However, these gains materialize only when evidence generation is embedded in end-to-end delivery systems.

Within this context, the World Bank is developing a global knowledge report under the PASA “From Intelligence to Impact: AI in Agricultural Extension and Payment for Ecosystem Services (P508786).” The report examines how AI-enabled digital measurement, reporting, and verification (dMRV) can make evidence actionable at scale for SLM-related incentive schemes (including PES-type arrangements where relevant)—clarifying what is operationally feasible, what minimum conditions must be in place, and where common failure modes arise in smallholder and mixed-farming settings. It also considers how improved verification readiness may, in some contexts, support more credible assessment of whether selective carbon-credit pathways are relevant to incentive programs. The central challenge is not lack of ambition; rather, monitoring and verification approaches remain costly and operationally brittle as programs expand—often pushing schemes toward practice-based payments with weak feedback loops and limited ability to recalibrate incentives based on measured performance.

To develop a technically solid and operationally realistic technical core, the Bank will engage two complementary short-term consultants who will work closely: (i) an EO/ML and delivery-workflow specialist (this assignment), and (ii) an MRV/validation specialist. This consultant will lead development of EO/ML and delivery-workflow content for the shared technical note (use cases, evidence patterns, minimum conditions, and technical failure modes). The companion MRV/validation consultant will lead consolidation and editing to ensure verification realism, audit-ready evidence packaging requirements, and disciplined claims language. Together, they will ensure coherence with the report’s portfolio evidence and recommendations, and coordinate as needed on the interface with the report’s cautious treatment of carbon-credit pathway considerations.

Objective:

The objective of this consultancy is to provide technically robust and implementation-grounded inputs for the report’s technical core chapter on AI-enabled dMRV for SLM-related incentive delivery, with emphasis on

(i) EO/ML-enabled monitoring and risk flagging that is operational at program scale, and

(ii) digital delivery workflows that link enrollment/and plot definition to repeatable evidence, targeted validation, and verification readiness in smallholder settings. The consultant will provide EO/ML and workflow inputs to the shared technical note using an agreed structure and consistent terminology.

The consultant will also support consistency of verification-related terminology and minimum conditions across the technical core and other relevant report sections (e.g., portfolio evidence and recommendations). The assignment should help clarify what is technically observable, what is operationally decision-useful, and what remains conditional on stronger validation or delivery systems.

Duties and Responsibilities:

Under the supervision of the report lead and in coordination with the companion MRV/validation STC and the climate/carbon team, the consultant will:

A. Define “what became operational” for SLM/PES delivery

• Synthesize deployable EO/ML capabilities for SLM-related incentive operations (e.g., high-frequency monitoring of land cover/vegetation dynamics; practice-relevant signals and risk flags for targeting, supervision, and performance tracking).

• Translate capabilities into fit-for-purpose layered evidence strategies (digital indicators targeted field validation), avoiding accuracy benchmarking and focusing on decision utility and operational feasibility.

B. Specify minimum conditions and failure modes for credible use

• Document minimum operational conditions needed for credible deployment (e.g., enrollment/plot linkage, QA/QC routines, ground validation capacity, data governance/ and dispute-handling touchpoints, where relevant).

• Identify common failure modes (top 3 per priority use case) that break credibility or slow cycle time.

C. Contribute to evidence packaging and cycle-time realism

• Provide inputs to a cost & cycle-time driver map (where time accumulates; what digital workflows can reduce vs cannot reduce).

• Contribute EO/ML–relevant elements of “verification readiness” (traceability, evidence logs, versioning, documentation, and workflow handoff points for validation/verification).

D. Conduct structured practitioner interviews (for technical reality checks)

• Lead three structured interviews with implementers/technology providers and/or program delivery teams (categories agreed with report lead), and produce standardized one-page interview memos.

• Provide EO/ML and workflow draft inputs for the shared Technical Note A (8–12 pages total), using one consistent structure: Use case → Evidence pattern → Minimum conditions → Common failure modes → Claim label (Decision-ready / Conditional / M&E).

Selection Criteria:

The ideal candidate will possess the following qualifications and experience:

• Advanced degree in remote sensing, environmental science, data science, agriculture, or related field.

• Minimum 6–8 years of applied experience in EO/remote sensing and/or ML workflows used for operational monitoring (not only academic research).

• Demonstrated experience translating EO/ML outputs into program operations (e.g., targeting, supervision, risk flagging, or validation support) in low-capacity delivery contexts.

• Demonstrated experience designing repeatable EO/ML monitoring workflows for program operations (e.g., monitoring frequency, QA/QC routines, and traceability/versioning).

• Experience linking enrollment/plot delineation and georeferencing workflows to monitoring signals and evidence generation (including practical constraints and failure modes).

• Strong writing and synthesis ability for policy/implementation audiences, including ability to produce implementation-facing guidance (use cases, minimum conditions, failure modes, and “what is realistic” language).

• Experience working across technical and operational audiences, including the ability to distinguish what is technically observable from what is operationally decision-useful.

Terms of the contract: This assignment will run from May 1, 2026 to June 30, 2026, for a total of 30 working days. The consultant will report to Jeehye Kim (Senior Agricultural Economist; ADM TTL) and coordinate with Kichan Kim (Agricultural Economist Consultant; report lead), working closely with the SAEA2 Uganda team and the companion STC. The consultant will participate in a weekly joint check-in with the companion STC and may join up to two alignment calls with the climate/carbon team as needed. The assignment will be remote/home-based with virtual meetings. All deliverables will be reviewed and finalized in consultation with the TTL and report lead. Additional days may be agreed if additional alignment support is requested, subject to budget and approvals.