Phase 4: Visualization and Final Recommendations

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/MajoRodri/HRIA/llms.txt

Use this file to discover all available pages before exploring further.

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Notebook

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Libraries

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Visualizations

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Sample Visualization Code

import seaborn as sns
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6))
sns.boxplot(
    data=df_sal,
    x='formatted_experience_level',
    y='salary_annual',
    order=['entry level', 'associate', 'mid-senior level',
           'director', 'executive'],
    palette='Blues'
)
ax.set_title('Annual Salary by Experience Level')
ax.set_xlabel('Experience Level')
ax.set_ylabel('Annual Salary (USD)')
plt.tight_layout()
plt.show()

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Reskilling ROI Analysis (Viz 11)

• Current median salary at entry level as the baseline
• Median salary at mid-senior level for each target role as the destination
• Skill gap complexity (estimated as the number of top-10 required skills not typically held by general IT graduates) as a proxy for reskilling cost

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Key Visual Findings

• Salary distribution is approximately normal (skew = 0.442, kurtosis = −0.248), validating the use of parametric tests in Phase 3. The lognormal fit (Viz 1.1) is marginally inferior to the normal fit within the IQR-cleaned range.
• Director and executive roles show the highest median salary but also the highest variance — the boxplot whiskers extend significantly further than lower levels, reflecting the wide range of company sizes and industries that carry these titles.
• Tech skills dominate demand across all data roles: IT infrastructure skills and DATA-specific skills (Python, SQL, ML frameworks) account for the top positions in both the global ranking and the Spain-specific ranking shown in Viz 3, though cloud platform tools (particularly Azure) rank higher in the Spanish segment.
• Views and applications are positively correlated (Viz 5), but the relationship is noisy — some postings accumulate views without applications, likely reflecting job-seeker interest without perceived fit or accessibility.

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Final Recommendations Summary

1. Focus recruitment on mid-senior data engineers and ML engineers — these roles show the strongest combination of market demand and salary premium within the data talent landscape.
2. Invest in reskilling programs targeting junior → data role transitions — the ROI analysis (Viz 11) demonstrates measurable and quantifiable salary uplift, making the business case for internal training programs straightforward to present to stakeholders.
3. Adjust all salary benchmarks for the Spanish market — the LinkedIn dataset is US-centric (> 95 % US postings). Spanish market salaries for equivalent data roles are typically 40–60 % of the US figures shown here; applying US benchmarks directly would result in uncompetitive offers.