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# AI/ML Developer Consultant

- **Company:** United Nations
- **Location:** Wien, Austria
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Data Analysis, Asana, Data Cleansing, Extract Transform Load (ETL), Document-Oriented Databases, Python (Programming Language), Machine Learning, Natural Language Processing, SQL Databases, Test Data, Kubernetes, Information Technology, Microservices
- **Published:** June 13, 2026
- **Apply:** https://at.indeed.com/viewjob?jk=b8249fa54aa1a4cc

## About the Role

Do you have a Master's degree?, Advanced university degree (Master's degree or equivalent) in computer science or related field is required. A first-level university degree in similar fields in combination with 2 years of qualifying experience may be accepted in lieu of the advanced university degree is required. Seven years of overall experience in artificial intelligence, machine learning, predictive models, graph analytics, Python, SQL, Kubernetes, Microservices, Asana, predictive modelling, forecasting, NLP.
Languages
English and French are the working languages of the United Nations Secretariat. For this post, fluency in oral and written English is required. Knowledge of another official United Nations language is an advantage. Knowledge of French is desirable

## Description

Under the direct supervision of the Chief, the Consultant will complete specific tasks that are required to produce the detailed system design. The solutions may include text field vectorization or word embedding algorithms, classification models, interpretation algorithms, may include a front-end part with a wizard to enable necessary configuration (selection of table fields, parametrization of retraining, etc). Design: Understand data sources and STR structures. Assist in defining the logical Analytical Base Table (ABT). Preparation: Develop ETL scripts for ABT creation. Identify and document data quality issues. Implement data cleaning and transformation pipelines. Analysis: Conduct exploratory data analysis (EDA). Generate correlation matrices, class imbalance reports, and variable importance metrics. Modelling: Build and validate supervised ML models. Address class imbalance using appropriate techniques. Implement model interpretability tools. Design: Technical architecture design
Data: Acquiring / preparing test data Development: Testing algorithmic logic on data Development: Refining algorithmic logic based on test feedback Development of back-end with the approved algorithmic logic Development of front end Testing: Conduct model testing and performance evaluation. Refine models based on feedback and test results. Testing front-end and back-end Deployment: Running minimum viable product (MVP) with an FIU Documenting


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