Online Volunteer - Machine Learning

Job Posting Organization:
The United Nations Development Programme (UNDP) is a global organization established to support countries in their efforts to achieve sustainable development and eradicate poverty. The Bureau for Policy and Programme Support (BPPS) is a key component of UNDP, responsible for developing policies and guidance that align with UNDP’s Strategic Plan. The organization emphasizes innovation, knowledge sharing, and data-driven approaches to enhance development outcomes. UNDP operates in numerous countries worldwide, providing technical advice and support to Country Offices, engaging in multi-stakeholder dialogues, and coordinating with various UN agencies to address global challenges.

Job Overview:
The Online Volunteer position focuses on contributing to the research and development of intelligent document processing (IDP) within the BPPS data team. The role requires a skilled individual with a background in machine learning to assist in analyzing and processing documents using advanced ML techniques. The selected volunteer will engage in a desk review of existing literature and frameworks related to IDP, prototype ML models for semantic document layout analysis, and document their findings in a concise research paper. This position is designed for individuals who are passionate about leveraging technology to improve data processing and contribute to sustainable development goals.

Duties and Responsibilities:

• Conduct a comprehensive desk review of existing literature, frameworks, and advancements in intelligent document processing (IDP).
• Prototype machine learning models for semantic document layout analysis using provided datasets.
• Collaborate with the Data Science Specialist to refine research methodologies and approaches.
• Document research findings and experimental results in a well-structured short research paper.
• Provide insights and recommendations based on the analysis of data and findings.
• Engage in regular communication with the BPPS data team to ensure alignment with project goals and objectives.
• Contribute to knowledge sharing and discussions on innovative approaches to data processing and analysis.

Required Qualifications:

• Proven experience in machine learning, particularly in the context of document processing and analysis.
• Strong understanding of semantic analysis and document layout techniques.
• Familiarity with programming languages and tools commonly used in machine learning, such as Python, R, or similar.
• Ability to conduct thorough literature reviews and synthesize findings into actionable insights.
• Excellent written communication skills, with the ability to articulate complex concepts clearly and concisely.
• Experience in writing research papers or technical documentation is highly desirable.

Educational Background:
A degree in computer science, data science, artificial intelligence, or a related field is required. Advanced degrees (Master's or PhD) are preferred but not mandatory. Candidates should have a solid foundation in machine learning principles and practices, as well as experience with relevant tools and technologies.

Experience:
Candidates should have documented experience in machine learning, particularly in the area of intelligent document processing. Experience in conducting research, prototyping ML models, and writing technical papers is essential. Previous involvement in projects related to data analysis or document processing will be advantageous.

Languages:
Proficiency in English is mandatory, as all documentation and communication will be conducted in this language. Knowledge of additional languages may be considered an asset but is not required for this position.

Additional Notes:
This is a remote volunteer position, and the duration of the engagement is flexible based on the project needs and the volunteer's availability. The role is part-time and does not offer financial compensation, but it provides valuable experience and the opportunity to contribute to meaningful research in the field of machine learning and sustainable development.