Hiring Process Analytics

Click here for Excel Data Set

The objective of this project is to conduct a thorough analysis of the primary underlying trends related to the hiring process. The hiring process stands as a paramount function within any company. Multinational corporations gain insights into hiring trends, encompassing factors like salary distribution, class intervals, job types, vacancies, and post-tier distribution. Analysing such trends is crucial for a company's decision-making when considering fresh hires or any other candidates. 

Approach

The methodology employed in the analysis of the provided dataset consists of the following steps: Initially, data cleansing procedures are executed to rectify any missing entries, eliminate duplicates, and ensure appropriate formatting. Subsequently, outlier detection techniques are applied to identify data points that may significantly influence the analysis. Following this, pertinent statistical computations are conducted to obtain a comprehensive understanding of the dataset. Finally, valuable insights are derived through statistical analysis, and these findings are effectively visualized using established data visualization techniques such as graphs and charts.

Please refer to the attached Excel sheet 1 to see the process of data cleaning and outlier computation.

Hiring Process Analytics

1.Hiring Analysis:

The hiring process involves bringing new individuals into the organization for various roles.

Task: Determine the gender distribution of hires. How many males and females have been hired by the company?

Insight – 55% of the hired candidates are male, while only 39% are female, with the remaining candidates not disclosing their gender. A high gender ratio skewed towards males could potentially harm the organization's public image. Therefore, efforts should be directed towards reducing this gender imbalance and striving for a ratio closer to 1:1. Priority must be given to ensure the completeness and relevance of the data. Incomplete or irrelevant data could impede the analysis process and lead to inaccurate conclusions.

2.Salary Analysis:

The average salary is calculated by adding up the salaries of a group of employees and then dividing the total by the number of employees.

Task: What is the average salary offered by this company? Use Excel functions to calculate this.

Insight – The average of total offered salary is Rs.49,880, while the average salary of hired candidates is Rs.49,596. The average salary of hired candidates closely aligns with the offered salary, suggesting that the hiring team is recruiting candidates within the organization's salary guidelines.

3.Salary Distribution:

Class intervals represent ranges of values, in this case, salary ranges. The class interval is the difference between the upper and lower limits of a class.

Task: Create class intervals for the salaries in the company. This will help you understand the salary distribution.

Insight: Notably, the highest offered salaries are within the Rs.40,001–50,000 range, while the lowest offered salaries are distributed among the Rs.90,001–1,00,000 and Rs.1-10,000. This indicates a predominant demand for roles requiring moderate experience levels, with fewer opportunities available for both senior and entry-level positions.

4.Departmental Analysis:

Visualizing data through charts and plots is a crucial part of data analysis.

Task: Use a pie chart, bar graph, or any other suitable visualization to show the proportion of people working in different departments.

Insight: At 39%, the Operations Department leads in candidate hires, followed by the Services and Sales Departments. Conversely, the Human Resources Department registers the lowest number of hires at 1.49%. These statistics potentially signify the varying team sizes and the respective importance of each department within the organization.

5.Position Tier Analysis:

Different positions within a company often have different tiers or levels.

Task: Use a chart or graph to represent the different position tiers within the company. This will help you understand the distribution of positions across different tiers.

Insight: The analysis makes it apparent that the organization has recruited the highest number of candidates for post tier c9 at 1240, followed by c5 at 1182, with position i7 trailing in a distant third place at 635. Post tier n6 has the lowest count with only 1 employee.

Result: Engaging in this project has broadened my comprehension regarding the pivotal role of data analytics in the hiring processes of organizations. It furnished crucial insights encompassing gender and salary distribution, applicant profiles, and department data. These insights empower the hiring department to adopt a data-driven approach, facilitating more informed decision-making.