Consultant - Data Science & Machine Learning (Ground Water Salinization) at International Water Management Institute

, , Egypt -

Full Time

Start Date

Immediate

Expiry Date

04 Feb, 26

Salary

0.0

Posted On

06 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Machine Learning, Feature Engineering, Modeling, Forecasting, Geospatial Skills, Remote Sensing, Python, R, GIS, Hydrogeology, Water Quality, Communication, Active Learning, Documentation, Training

Industry

International Trade and Development

Description

The International Water Management Institute (IWMI) is an international, non-profit, research-for-development organization that works with governments, civil society, and private sector organizations to address water-related challenges in developing countries and scale up solutions. Headquartered in Colombo, Sri Lanka, IWMI is a CGIAR research center with offices in 15 countries and a global network of researchers operating in about 56 countries. The Water Scarcity Initiative (WSI) is a FAO-led regional program designed to support countries in the Near East and North Africa in addressing critical water challenges under conditions of scarcity. It promotes sustainable water management by combining policy dialogue, innovation, and field-based actions to enhance agricultural productivity, build climate resilience, and secure food and water resources. The initiative serves as a collaborative platform for governments, research institutions, and partners to co-develop solutions that balance water, food, and environmental needs. IWMI is seeking a consultant to design, build, and operationalize a reproducible Artificial Intelligence/Machine Learning (AI/ML) pipeline that mimics and forecasts groundwater salinity and produces a modeling framework for decision-grade risk maps and scenarios. DUTIES & RESPONSIBILITIES: A. Data Engineering & Governance Contribute to dataset collection. Curate and version datasets (e.g., well logs, EC/TDS/ions, groundwater levels, abstraction, soils, geology, climate, remote sensing, distance to coast, land use/irrigation, etc.). Implement tidy data schemas, metadata standards, and QA/QC procedures; manage a central repository; and ensure full reproducibility. B. Feature Engineering (Groundwater-Aware) Build features linking processes to salinity, such as groundwater level trends, exploitation ratios, rainfall/ET anomalies, irrigation intensity, return-flow proxies, coastal proximity/heads, soil/parent material indicators, and terrain metrics. Test lagged features and seasonal windows; handle spatial autocorrelation and site effects. C. Modeling & Forecasting Build, train, and benchmark predictive and time-series models. Perform cross-validation that respects space-time leakage constraints; quantify uncertainty; and implement model explainability. Co-develop scenario engines (e.g., reduced pumping, irrigation expansion, drier climate, sea-level/coastal head changes) and generate spatio-temporal risk projections. Generate predictive maps of groundwater quality parameters across regions and aquifers using GIS and remote sensing inputs. D. Products & Dissemination Produce decision-ready outputs, including risk maps, ranked hotspots, driver diagnostics, trend charts, and simple web/Tableau/Streamlit dashboards (as requested). Develop a concise handout describing the executed steps and the model run methodology. Contribute figures and text to policy briefs, methodological notes, and scientific/policy papers. Conduct two hands-on training sessions for counterparts on data and code usage. E. ML Ops & Quality Automate the pipeline using Active Learning; add tests; and ensure thorough documentation. Adhere to data privacy, licensing, and ethical AI/use policies. EDUCATIONAL QUALIFICATIONS: MSc/PhD in Data Science/Statistics/Computer Science or Hydro-informatics/Water Resources (or closely related). KNOWLEDGE & EXPERIENCE: Essential: Over 5 years of experience in building machine learning (ML) models addressing environmental, water, or groundwater-related challenges (e.g., salinity, water quality, hydrology, irrigation, or coastal intrusion). Expertise in the following subject areas are required: 1. Data Science and AI/ML Expertise Proficiency in machine learning (ML) for time-series and spatial prediction, including deep learning (ANN/DNN, CNN, RNN, LSTM, GRU, CNN-LSTM), ensemble and kernel methods (Random Forest, Gradient Boosting, SVM), and evolutionary/symbolic methods (e.g., GEP). Experience with Python and/or R for data processing, model development, and visualization. Strong understanding of feature engineering and techniques for handling missing, sparse, or imbalanced datasets commonly encountered in environmental monitoring. Ability to interpret and effectively communicate uncertainty in model predictions. 2. Hydrogeological and Water Quality Domain Knowledge Solid understanding of groundwater systems, aquifer types (e.g., karstic, alluvial), recharge dynamics, and contamination processes. Knowledge of key groundwater quality indicators (e.g., EC, nitrate, chloride, heavy metals, salinity) and their environmental drivers. Familiarity with both anthropogenic and natural contamination sources, including wastewater, agricultural activities, seawater intrusion, and landfill leachate. 3. Geospatial and Remote Sensing Skills Ability to integrate GIS datasets (e.g., land use, geology, DEMs, hydrology) and satellite data (e.g., rainfall from CHIRPS or IMERG, evapotranspiration, land cover). Experience in applying spatial interpolation techniques for mapping sparse monitoring data. Working knowledge of geospatial tools such as QGIS or ArcGIS, and platforms such as Google Earth Engine (GEE) or the GEE Python API. SKILLS & ABILITIES: Essential: Excellent communication skills and working proficiency in English. Desirable: Exposure to groundwater modeling tools (e.g., MODFLOW, SEAWAT) and hybrid ML–physics approaches, as well as Bayesian methods, Google Earth Engine, PostGIS, and simple dashboarding tools (e.g., Streamlit, Dash). Experience working in the MENA region context. Knowledge of French is considered an asset. This is a globally recruited consultancy position; therefore, individuals with relevant expertise are encouraged to apply. IWMI offers a competitive remuneration rate for this assignment. The duration of the contract shall be seventy-six (76) working days distributed over a period of eleven (11) months. How to Apply: Apply for the position by following the application instructions at www.iwmi.org/jobs. Applications will be accepted until 24:00 (IST) on November 13, 2025 (applications will be reviewed on a rolling basis). Your application must include a CV, cover letter, and three (3) references. All applications will be acknowledged, but only shortlisted candidates will be contacted. IWMI believes that diversity fuels our innovation, enhances our excellence, and is essential to our mission. We offer a multicultural, multicolor, multigenerational, and multidisciplinary working environment. We are committed to creating an inclusive organization that reflects our global character and our dedication to gender equity. We, therefore, encourage applications from individuals of all cultures, races, ethnicities, religions, sexes, national or regional origins, ages, disability statuses, sexual orientations, and gender identities.

Responsibilities

The consultant will design, build, and operationalize an AI/ML pipeline to forecast groundwater salinity and create decision-grade risk maps. Responsibilities include data engineering, feature engineering, modeling, and dissemination of results.