Python Data Scientist — AI Model Training & RL Optimization

Hired; Mexico; On-site; PHP 4,389,000 - 7,524,000; Full time

Job summary

Hired is seeking a Data Scientist/Analyst to join on a full-time basis to develop and maintain Python code for training AI models and to benchmark performance. You will collaborate with researchers and annotators to implement reinforcement learning with human feedback and refine reward models.

The role emphasizes evaluating model outputs, producing clear explanations of evaluations, and leading supervised fine-tuning with high-quality datasets for various tasks.

Qualifications

• Proficiency in Python for data analysis and model development is required.
• Experience with data science and machine learning techniques is required.
• Ability to extract insights from public data using analytical skills is required.
• Strong bug-fixing skills and ability to create thorough technical documentation are required.
• Excellent communication skills to articulate reasoning in notebooks or similar formats are required.

Responsibilities

• Design, develop, and maintain efficient, high-quality code to train and optimize AI models.
• Conduct evaluations to benchmark model performance and analyze results for continuous improvement.
• Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria.
• Develop explanations and rationales for evaluations, showcasing reasoning and technical expertise.
• Lead efforts in supervised fine-tuning, including creating and maintaining high-quality, task-specific datasets.

Skills

Python

Data analysis

Machine learning

Bug fixing

Technical documentation

Communication

Job description

Hired is seeking a Data Scientist/Analyst to join on a full-time basis to develop and maintain Python code for training AI models and to benchmark performance. You will collaborate with researchers and annotators to implement reinforcement learning with human feedback and refine reward models.

The role emphasizes evaluating model outputs, producing clear explanations of evaluations, and leading supervised fine-tuning with high-quality datasets for various tasks.