1. Years of Experience: 4.7 years of experience mentioned in the summary. Score: 8/10
2. Experience Summary: A professional summary of the candidate's experience is provided. Score: 10/10
3. Skills: Skills related to data and quantitative analysis, data science, big data analytics, predictive/statistical modeling, data mining, data visualization, machine learning algorithms, business intelligence, and more are mentioned. Score: 9/10
4. Role: The roles of Lead Consultant and Data Scientist are clearly mentioned with responsibilities and achievements. Score: 10/10
5. Role Type: The candidate has played functional and technical roles in data analytics and machine learning. Score: 8/10
6. Domain/Industry: The resume does not mention any specific domain or industry knowledge. Score: 0/10
7. Projects Executed: The resume provides information about the projects the candidate has worked on, including project management, data collection and preparation, data analysis, problem-solving, and more. Score: 9/10

Overall descriptive explanation for the information completeness factor:
- The candidate's years of experience and experience summary are clearly stated.
- The skills section provides a good overview of the candidate's technical abilities.
- The roles and responsibilities are well-defined and showcase the candidate's experience.
- The resume lacks information about the specific domain or industry knowledge.
- The projects executed section provides details about the candidate's project experience.

Overall, the resume provides a clear understanding of the candidate's experience and skills, with some areas lacking specific information.