1. Roles & Responsibilities: 8/10  
2. Years of Experience: 10/10  
3. Education Qualification: 4/10  
4. Skills/Knowledge: 10/10  
5. Domain/Industry: 8/10  
6. Key Competencies: 8/10  
7. Certifications: 4/10  

<h5 style="font-family: Roboto; font-size: 0.88rem;"><b>Overall Summary</b></h5>
<ol style="font-family: Roboto; font-size: 0.88rem;">
  <li><b>Roles & Responsibilities:</b> The candidate has demonstrated experience in designing and automating reports, performing data analysis, and collaborating with cross-functional teams during internships at PwC, MERISKILL, and ETLHIVE. They have also worked on data cleaning, transformation, and visualization using Power BI and Python, aligning well with the job's responsibilities. However, documentation and modular code writing are not explicitly mentioned, hence a slightly reduced score.</li>
  <li><b>Years of Experience:</b> The resume clearly indicates internship experiences and academic projects, which fall within the 0–1 year experience bracket. This is a perfect match.</li>
  <li><b>Education Qualification:</b> The candidate holds a Bachelor of Science and a Master of Science from Shivaji University. However, the requirement specifies a Bachelor of Engineering (BE) or equivalent in Computer Science, IT, or related disciplines. A BSc in Statistics is not a direct match, hence a lower score.</li>
  <li><b>Skills/Knowledge:</b> The candidate has strong alignment with the required skills: Python (with Pandas, NumPy, Matplotlib), SQL (including MySQL Workbench), Power BI (dashboard creation, DAX basics), and Excel (pivot tables, VLOOKUP, conditional formatting). These are explicitly mentioned and demonstrated in both internships and projects.</li>
  <li><b>Domain/Industry:</b> The candidate has worked in data analytics roles and has exposure to data engineering concepts such as ETL and data governance. While not deeply embedded in the IT/Data Engineering industry, the exposure is sufficient for an entry-level role.</li>
  <li><b>Key Competencies:</b> The resume reflects problem-solving (e.g., improving decision-making, reducing data retrieval time), communication (collaborating with teams), and attention to detail (data quality, performance monitoring). Proactive learning is implied through willingness to learn tools like Tableau and cloud platforms. Documentation and modular code writing are not explicitly mentioned.</li>
  <li><b>Certifications:</b> There is no mention of formal certifications in Python, SQL, Power BI, or Excel. While skills are demonstrated through projects and internships, the absence of certifications leads to a lower score.</li>
</ol>