Salary Negotiation

what's the problem?

As we enter the workforce (+ co-op and internship), a big question on a lot of people's mind is "how much will I be getting paid?" I think a better way of looking at this would be "how much can I realistically be paid?" Becoming equipped with this information will allow applicants to better understand their worth and aid in potential salary negotiations.

It can be intimidating to negotiate salary, but with an understanding industry standards, and the pay towards similar jobs in different industries, some of the stress surrounding negotiations can be alleviated.

CNBC and Fidelity Investments claim that while 58% of working Americans accepted their initial job offer, 85% of the Americans who made attempts at negotiating their salaries were successful.

import pandas as pd
datajobs = pd.read_csv('datajobs.csv', encoding = 'latin-1')

datajobs

	company	job title	location	job description	salary estimate	company_size	company_type	company_sector	company_industry	company_founded	...	python_yn	spark_yn	azure_yn	aws_yn	excel_yn	machine_learning_yn	job_simpl	seniority	description_len	company_age
0	Microsoft	Data & Applied Scientist	Redmond, WA	Microsoft 365 is a key part of the company’s c...	123486	10000+ Employees	Company - Public	Information Technology	Computer Hardware Development	1975.0	...	1	0	0	1	0	1	data scientist	junior	359	47.0
1	UT Southwestern Medical Center	Data Scientist or Bioinformatician (remote)	Remote	Center Information:\nThe Quantitative Biomedic...	93500	10000+ Employees	Hospital	Healthcare	Health Care Services & Hospitals	1943.0	...	1	0	0	0	0	1	data scientist	mid	267	79.0
2	Notion	Data Scientist, Growth	New York, NY	About Us:\nWe're on a mission to make it possi...	137853	201 to 500 Employees	Company - Private	Information Technology	Enterprise Software & Network Solutions	2016.0	...	1	0	0	0	0	0	data scientist	Senior	589	6.0
3	Net2Aspire	Jr. Data Scientist	Remote	? Apply Statistical and Machine Learning metho...	72500	Unknown	Company - Public	NaN	NaN	NaN	...	0	0	0	0	0	1	data scientist	junior	132	NaN
4	Ntropy Network	Data Scientist	Remote	Over the last few decades, technological innov...	155000	1 to 50 Employees	Company - Private	NaN	NaN	NaN	...	1	0	0	1	0	0	data scientist	mid	522	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2079	YouTube	Staff Software Engineer, Machine Learning, You...	San Bruno, CA	Minimum qualifications:\nBachelor's degree or ...	141704	1001 to 5000 Employees	Subsidiary or Business Segment	Information Technology	Internet & Web Services	2005.0	...	0	0	0	0	0	1	machine learning engineer	Senior	498	17.0
2080	Hunter Engineering	Data Science Co-Op	Bridgeton, MO	Overview:\nDo you have a passion for data scie...	88383	1001 to 5000 Employees	Company - Private	Manufacturing	Machinery Manufacturing	1946.0	...	1	0	0	0	1	1	other	Senior	349	76.0
2081	precision technologies corp	Jr UI/UX Designer Training and Placement	Remote	If you want to start your IT career as a UI/UX...	70600	201 to 500 Employees	Company - Private	Information Technology	Information Technology Support Services	2008.0	...	1	0	0	1	1	1	other	junior	391	14.0
2082	Argonne National Laboratory	Postdoctoral Appointee - Probabilistic Machine...	Lemont, IL	The Mathematics and Computer Science Division ...	54291	1001 to 5000 Employees	Government	Management & Consulting	Research & Development	1946.0	...	0	0	0	0	0	1	machine learning engineer	Senior	506	76.0
2083	Colossal Biosciences	Graduate Research Fellow, Machine Learning – S...	Dallas, TX	The Machine Learning Graduate Research Fellow ...	66609	1 to 50 Employees	Company - Private	NaN	NaN	NaN	...	1	0	0	0	0	1	machine learning engineer	Senior	376	NaN

2084 rows × 23 columns

This dataset includes information (company, title, descriptions, location, requirements, salary, etc) of just over 2,000 data related jobs. My hope is to be able to work with the value_counts() function to try to find common words in the job description and job titles columns, and use these in conjunction with the other columns as features in determining expected salary of the job.

In the end, if someone were to input a dataset with jobs in the format of these columns, the intended output would be that job's expected salary. The goal would be to allow for a greater sense of salary transparancy (to the applicant) and provide them with the information that could serve useful in negotiations.

data_dict = {'company':'company name',
             'job title':'job title',
             'location':'office location, when available',
             'job description':'all available details regarding the position',
             'salary estimate':'average annual salary',
             'company_size':'approximation of # of employees at company',
             'company_type':'classification (public, private, etc)',
             'company_sector':'job/company sector',
             'company_industry':'company industry',
             'company_founded':'year company was founded',
             'company_revenue':'company annual revenue',
             'hourly':'whether the pay is hourly or not',
             'rating':'company rating on glassdoor',
             'python_yn':'is python a required skill?',
             'spark_yn': 'is spark a required skill?',
             'azure_yn':'is azure a required skill?',
             'excel_yn': 'is excel a required skill?',
             'machine_learning_yn':'is machine learning a required skill?',
             'seniority':'position ranking in company hierarchy',
             'description_len':'length of job description (word count)',
             'company_age':'how many years the company has been in business'}