Project Overview

• Build a prototype that evaluates financial issuers using AI-based sentiment analysis and risk scoring models for market intelligence.
• Focus on combining NLP sentiment signals and quantitative risk scoring to produce market-intelligence-ready issuer assessments.
• Estimated Duration: 4-6 months; Team: 1 Intern.

Responsibilities / Tasks

• Ingest and process financial and market text/data into Azure Data Lake for downstream analysis.
• Implement AI-based sentiment analysis (leveraging OpenAI) and develop risk scoring models based on extracted features.
• Develop a prototype front-end/back-end using .NET and Angular to present issuer risk dashboards and visualizations.
• Build Power BI reports/dashboards to surface sentiment, risk scores, and market intelligence insights.

Technologies & Tools

• Core technologies: OpenAI (for NLP/sentiment), Azure Data Lake (data storage/processing), Power BI (visualization).
• Development stack: .NET (backend services), Angular (frontend), integration with Azure services and BI tools.

Deliverables & Expected Outcomes

• Working prototype that scores issuers and displays sentiment/risk intelligence via a web UI and Power BI dashboards.
• Documentation of data pipelines, model design, evaluation metrics, and instructions for deployment.
• Demo presentation of the prototype and sample reports showing issuer risk insights.

Required / Recommended Skills

• Experience with NLP/ML workflows and familiarity with sentiment analysis techniques (preferably applied to financial text).
• Practical knowledge of Azure data services (Azure Data Lake), .NET development, and Angular-based front-end implementation.
• Ability to produce Power BI reports and to integrate model outputs into interactive dashboards.

How to apply

• Apply online via the job posting: https://secure.collage.co/jobs/finsotech/57794
• Or send your application by email to recrutementunisie@finlogik.com with the subject line: "Application - TOPIC 3 : AI-POWERED ISSUER RISK MANAGEMENT TOOL PFE".