Real Estate Finance – AI Trainer

Real Estate Finance – AI Trainer
نوع العمل : عمل كلى
الخبرة : 0-1 سنة
الراتب : not
المكان : egypt

Job Title: Real Estate Finance - AI Trainer


Job Type: Part-time


Location: Remote


Job Summary:


Join our customer's team as a Real Estate Finance - AI Trainer and play a pivotal role in shaping next-generation AI models for finance. Engage directly with complex, real-world real estate finance data and artifacts, bringing your expertise to projects where ambiguity and partial structure are the norm. Help seed and refine large-scale datasets powering AI solutions that will transform how finance professionals work.


Key Responsibilities:


• Analyze and interpret messy, real-world spreadsheets and in-the-wild finance artifacts for AI training.

• Seed data for AI model development with minimal upfront structuring, contributing to iterative flows.

• Collaborate with finance-adjacent contributors to source, normalize, and extend datasets.

• Design and document the creation of partial models and address ambiguous data inputs.

• Communicate findings, challenges, and strategies effectively in both written and verbal formats.

• Advise on best practices for data seeding and annotation in finance-related AI training projects.

• Support the evaluation and improvement of model output for accuracy and relevance.


Required Skills and Qualifications:


• Strong background in real estate finance or related financial sector.

• Excellent written and verbal communication skills, with a focus on clarity and precision.

• Demonstrated experience working with unstructured or messy data.

• Proficiency with spreadsheets and financial modeling.

• Ability to thrive in ambiguous, loosely structured project environments.

• Familiarity with AI, machine learning, or data annotation concepts.

• Detail-oriented, analytical, and a collaborative team player.


Preferred Qualifications:


• Experience in AI/ML training or similar data-driven roles.

• Knowledge of broader finance, investment, or real estate technologies.

• Prior work on complex or experimental data seeding projects.

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