Water Smart Agriculture Use Case Consultant, Data Science Program

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Summary of Assignment:
The primary responsibilities of the Water Smart Agriculture Use Case Consultant are to:
1.
Work closely with agriculture projects in LACRO, with particular emphasis on projects implementing WaterSmart Agriculture, to identify the questions/hypotheses and the aligned datasets for the Agriculture UseCase.
2.
Coordinate with the Data Science Technical Advisor and Use Case Lead to review datasets, clarifygaps, and address quality issues with project staff.
3.
Attend weekly meetings with the Use Case Lead, Data Scientist, Analysts, and Data ScienceTechnical Advisor to monitor data quality and reporting, review and escalate issues, and reviewroot cause analysis.
4.
Lead the coordination with regional and project staff on a weekly basis for ground truthing, toreview models result and interpretation, identifying any gaps in reporting or unexpected results.
5.
Assist in after-action review activities led by the Data Science Technical Advisor and Data ScienceDirector

Detailed Activities:
1.
Coordinate with project staff, technical advisors, and key Data Science Center of Practice (DSCOP)staff to identify the questions/hypotheses and internal and external datasets that align with theAgriculture Use Case learning questions and data requirements.
2.
Support project staff responsible for managing WSA datasets with the provision of datarequirements and setting data quality expectations of internal and external datasets.
3.
Coordinating with country and regional stakeholders and platforms for data use and learningfrom models – ensuring that quality issues are reported and that unexpected results areanalyzed.
4.
Coordinate with the Data Science Technical Advisor and Use Case Lead to conduct root causeanalysis on key data quality issues that arise.
5.
Participate in meetings with the Use Case Lead, Data Scientist, Analysts, and Data ScienceTechnical Advisor to monitor data quality and reporting, review and escalate issues, and reviewroot cause analysis.
6.
Assist key DSCOP staff and agriculture use case stakeholders on finalizing the following:
a.
Data requirements and cataloging of internal and external datasets;
b.
Critical data elements and prioritization (e.g., participant ID, name, location) essential fordata modeling and use;
c.
Data quality rules and metrics definitions for internal and external datasets;
d.
Processes for quality improvement of data in models and reporting, among others.
7.
Assist the Data Science Technical Advisor in conducting an after-action review of the process ofonboarding datasets, remediating data to ensure quality, interpreting and using results, andcapacity strengthening of staff interfacing with the data science program.