{ "cells": [ { "cell_type": "markdown", "id": "43cb46d1", "metadata": {}, "source": [ "**Business Problem Summary:**\n", "\n", "As a data scientist at a growing startup, one of the challenges is establishing a fair and consistent pay scale across all roles while considering employees' years of experience. Currently, each staff member negotiates their salary independently, and the company lacks both a structured pay scale and defined job levels.\n", "\n", "Currently, there's no structured pay system in place, and all employees negotiated their salaries individually, leading to lot of discrepancies and biases in offers to new hires.\n", "\n", "The aim is to introduce a transparent pay scale that categorizes roles based on years of experience, ensuring that new hires are not unfairly compensated compared to existing employees. \n", "\n", "\n", "**Business Problem Details:**\n", "1. **Creating a fair salary framework::** The objective is to create a fair and transparent pay scale that categorizes roles based on years of experience, ensuring consistency and equality in compensation across the organization.\n", " \n", "2. **Introduction of Job Levels:** By categorizing roles into job levels based on years of experience (e.g., 0-2 years as Analyst, 2-4 years as Associate, etc.), the organization aims to provide a structured framework for salary determination.\n", " \n", " \n", " **To arrive at a fair pay scale, salary data of current staff and their years of experiences are used**" ] }, { "cell_type": "code", "execution_count": 144, "id": "d6219b57", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 145, "id": "4a8a0d3a", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Datasets May-April\\Salary_dataset.csv\")" ] }, { "cell_type": "code", "execution_count": 158, "id": "fd225f51", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearsExperienceSalary
01.239344
11.446206
21.637732
32.143526
42.339892
\n", "
" ], "text/plain": [ " YearsExperience Salary\n", "0 1.2 39344\n", "1 1.4 46206\n", "2 1.6 37732\n", "3 2.1 43526\n", "4 2.3 39892" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 160, "id": "77fd912c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearsExperienceSalary
count30.00000030.000000
mean5.41333376004.000000
std2.83788827414.429785
min1.20000037732.000000
25%3.30000056721.750000
50%4.80000065238.000000
75%7.800000100545.750000
max10.600000122392.000000
\n", "
" ], "text/plain": [ " YearsExperience Salary\n", "count 30.000000 30.000000\n", "mean 5.413333 76004.000000\n", "std 2.837888 27414.429785\n", "min 1.200000 37732.000000\n", "25% 3.300000 56721.750000\n", "50% 4.800000 65238.000000\n", "75% 7.800000 100545.750000\n", "max 10.600000 122392.000000" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 168, "id": "7ad47920", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(data=df, x= 'YearsExperience', y = 'Salary')\n", "\n", "# at experience 0, the data shows the salary is just below 40000 (start pay)" ] }, { "cell_type": "markdown", "id": "29c6de92", "metadata": {}, "source": [ "**- Much as the pay has always been individually negotiated, there's a linear reationship between the years of experience and the salaries**" ] }, { "cell_type": "code", "execution_count": 169, "id": "f29338c4", "metadata": {}, "outputs": [], "source": [ "X = df['YearsExperience']\n", "y = df['Salary']" ] }, { "cell_type": "markdown", "id": "5629a089", "metadata": {}, "source": [ "**Because the dataset has only one Feature, the Simple Linear Regression will be used.**" ] }, { "cell_type": "code", "execution_count": 172, "id": "52da51a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 9449.96232146, 24848.20396652])" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# y = mx+b\n", "\n", "np.polyfit(X, y, deg=1)\n", "\n", "# the starting salary an 0-year expereince is #24848" ] }, { "cell_type": "markdown", "id": "0709d389", "metadata": {}, "source": [ "### Predict using the equation" ] }, { "cell_type": "code", "execution_count": 161, "id": "787e8653", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.31034483, 1.62068966, 1.93103448, 2.24137931,\n", " 2.55172414, 2.86206897, 3.17241379, 3.48275862, 3.79310345,\n", " 4.10344828, 4.4137931 , 4.72413793, 5.03448276, 5.34482759,\n", " 5.65517241, 5.96551724, 6.27586207, 6.5862069 , 6.89655172,\n", " 7.20689655, 7.51724138, 7.82758621, 8.13793103, 8.44827586,\n", " 8.75862069, 9.06896552, 9.37931034, 9.68965517, 10. ])" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pro_exp = np.linspace(1,10, 30)\n", "\n", "pro_exp" ] }, { "cell_type": "code", "execution_count": 162, "id": "3c7dcb6d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 34298.16628798, 37230.91321533, 40163.66014268, 43096.40707003,\n", " 46029.15399738, 48961.90092473, 51894.64785208, 54827.39477943,\n", " 57760.14170678, 60692.88863413, 63625.63556148, 66558.38248883,\n", " 69491.12941618, 72423.87634353, 75356.62327088, 78289.37019822,\n", " 81222.11712557, 84154.86405292, 87087.61098027, 90020.35790762,\n", " 92953.10483497, 95885.85176232, 98818.59868967, 101751.34561702,\n", " 104684.09254437, 107616.83947172, 110549.58639907, 113482.33332642,\n", " 116415.08025377, 119347.82718112])" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pred_sal = 9449.96232146*pro_exp + 24848.20396652\n", "pred_sal" ] }, { "cell_type": "code", "execution_count": 163, "id": "c99a8cd3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=df, x='YearsExperience', y='Salary')\n", "plt.plot(pro_exp, pred_sal, color='red')" ] }, { "cell_type": "markdown", "id": "ba8292ac", "metadata": {}, "source": [ "**The HR team approached me, saying we have 2 new cabdidates whose years of experiences are 10 and 5; asking how much the offer should be**" ] }, { "cell_type": "code", "execution_count": 182, "id": "79d5ecad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For a 10_year work experience, the salary offer should be: $ 119347.83\n" ] } ], "source": [ "exp = 10\n", "pred_sal = 9449.96232146*exp + 24848.20396652\n", "\n", "print(\"For a 10-year work experience, the salary offer should be: $\", round(pred_sal, 2))" ] }, { "cell_type": "code", "execution_count": 183, "id": "957b40b3", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For a 5-year work experience, the salary offer should be: $ 72098.02\n" ] } ], "source": [ "exp=5\n", "pred_sal = 9449.96232146*exp + 24848.20396652\n", "\n", "print(\"For a 5-year work experience, the salary offer should be: $\", round(pred_sal, 2))" ] }, { "cell_type": "markdown", "id": "b46f5125", "metadata": {}, "source": [ "### Conclusion:\n", "\n", "By aligning roles with these well-defined job levels, the organization provides a transparent roadmap for career advancement. By using the equation (y = m*(years of experience) + b), managers are able to negotiate fairly when hiring new talents- without ofshooting the payscale within the organization.\n", "\n", "\n", "1. **Fair and Transparent Pay Scale:** The proposed salary framework introduces a fair and transparent pay scale by using a polyfit equation derived from the array ([9449.96232146, 24848.20396652]). This equation, represented as y = mx + b, determines salaries (y) based on years of experience (x). By relying on this mathematical approach, the organization eliminates potential biases and ensures consistency in compensation across all roles and levels.\n", "\n", "2. **Well-Defined Career Progression:** The framework establishes clear job levels, enabling structured career growth and progression within the organization. These job levels are:\n", "\n", " - a. Analyst: Covering professionals with 0-2 years of experience in their field.\n", " - b. Associate: Encompassing individuals with 2-4 years of relevant experience.\n", " - c. Senior Associate: For those with 4-6 years of demonstrated expertise.\n", " - d. Lead: Recognizing professionals with 6-8 years of significant experience and leadership capabilities.\n", " - e. Manager: Reserved for seasoned individuals with 8-10 years of extensive experience and managerial responsibilities.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3afdf321", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "4a7a7229", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 5 }