Resume Dataset 200k Enhanced

By shrijeetverma13 · March 15, 2026

Data source:

PhD holders have the highest hiring success rate at 70.72%, making them the most likely candidates to be hired. A bar chart has been generated showing the hiring success rates across all education levels, making it easy to compare how each group performs.

The overall hiring rate across all candidates is 70.61%. The bar chart breaks down how this rate varies by company type, with a red dashed line marking the overall average for easy comparison. Each bar shows the hiring rate percentage for that specific company type, allowing you to quickly see which types hire above or below the overall average.

Hired candidates consistently score higher on skills assessments than non-hired candidates, and this pattern holds true across all three company types (MNC, Mid-size, and Startup). A grouped bar chart and data table have been generated to visualize these differences clearly.

The analysis examined how the number of certifications affects hiring rates across 200,000 candidates. A bar chart and data table were generated showing hire rates at each certification level, along with a marginal gain line that reveals whether additional certifications continue to improve hiring odds or plateau.

The analysis reveals hiring rates across four age groups (21-24, 25-28, 29-32, 33-35) broken down by company type (MNC, Mid-size, Startup). A grouped bar chart and detailed table were generated to visualize these patterns. The highest hiring rate belongs to the 25-28 age group at Startups (71.15%), while the lowest is seen in the 29-32 age group at Mid-size companies (70.21%). Overall, hiring rates are quite consistent across all groups, ranging narrowly between ~70.2% and ~71.2%.

A heatmap was generated showing how different combinations of internships and projects jointly affect hiring likelihood. The overall hire rate across all candidates is 70.6%. Candidates with 1 internship and 12 projects achieved the highest hire rate of 100% (among a group of 3), while those with 9 internships and only 1 project had the lowest at 0%. In general, having more projects — especially when paired with at least some internship experience — tends to boost hiring chances significantly.

No, soft skills score does NOT serve as a stronger hiring differentiator than skills score. In fact, skills score is clearly the more impactful factor. The analysis used Cohen's d (effect size) and correlation with hiring outcomes to compare both metrics.

University tier has virtually no impact on hiring rates across different company types. All combinations of university tier (Tier 1, 2, 3) and company type (MNC, Mid-size, Startup) show hiring rates tightly clustered between 70.0% and 71.0%. A grouped bar chart and heatmap were generated to visualize these patterns clearly.

The average profile of hired candidates is remarkably consistent across all three company types (MNC, Mid-size, and Startup). A grouped bar chart and detailed data table have been generated showing the average values for 12 features per company type.

Technically yes, but the difference is negligible. Hackathon participants have a hiring rate of 70.99% compared to 70.36% for non-participants — a gap of just +0.63 percentage points. Participants are only 1.01x as likely to be hired, meaning hackathon participation has virtually no measurable impact on hiring outcomes in this dataset.

The analysis shows average skills scores for hired vs. not-hired candidates across three education levels. A grouped bar chart and data table were generated to visualize the results. Consistently across all education levels, hired candidates score slightly higher than those not hired.

The analysis examined how having research papers affects hiring rates across different company types and education levels. Four data tables were generated showing the breakdown of hiring rates for candidates with and without research papers, segmented by both company type and education level.

The bar chart compares the average profile of hired vs. not-hired candidates across four key metrics. Hired candidates consistently score higher across all dimensions, though the differences are relatively modest.

The analysis produced a grouped bar chart and data tables comparing average soft skills scores between hired and not-hired candidates across different company types. The visualization clearly shows side-by-side comparisons for each company type, making it easy to spot where hiring decisions align most strongly with soft skills performance.

The overall hiring rate across all candidates is 70.61%. A bar chart has been generated showing how this rate varies by company type, with a dashed red reference line marking the overall average. Each bar displays the specific hiring rate percentage for that company type, making it easy to see which types hire above or below the overall average.

The experience range that maximizes hiring probability is 15.7 to 17.7 years, achieving a perfect 100% hire rate (5 out of 5 candidates hired). A bar chart has been generated showing hire rates across all experience ranges, making it easy to compare which ranges perform best.

The analysis explores how the number of internships relates to hiring success across different company types. A line chart was generated showing hiring rates as internships increase, broken down by company type, making it easy to compare trends visually.

A bar chart was generated showing the hiring rate broken down by how many programming languages each candidate knows. The visualization lets you compare hiring percentages across different language counts, making it easy to spot any upward or downward trend.

The analysis explored which experience years threshold maximizes hiring probability for each company type. A line chart was generated showing hiring rate (%) across experience year bins for each company type, making it easy to visually identify where hiring probability peaks for different organizations.