This statistic shows the most important aspects of a good CV according to surveyed United Kingdom (UK) recruitment consultants in 2015. All of the surveyed recruiters found not having typos, not having grammatical errors, and detailing your achievements to be important aspects of a CV.

This experiment data comes from a study that sought to understand the influence of race and gender on job application callback rates. The study monitored job postings in Boston and Chicago for several months during 2001 and 2002 and used this to build up a set of test cases. Over this time period, the researchers randomly generating resumes to go out to a job posting, such as years of experience and education details, to create a realistic-looking resume. They then randomly assigned a name to the resume that would communicate the applicant's gender and race. The first names chosen for the study were selected so that the names would predominantly be recognized as belonging to black or white individuals. For example, Lakisha was a name that their survey indicated would be interpreted as a black woman, while Greg was a name that would generally be interpreted to be associated with a white male.

Details Because this is an experiment, where the race and gender attributes are being randomly assigned to the resumes, we can conclude that any statistically significant difference in callback rates is causally linked to these attributes.

Do you think it's reasonable to make a causal conclusion? You may have some health skepticism. However, do take care to appreciate that this was an experiment: the first name (and so the inferred race and gender) were randomly assigned to the resumes, and the quality and attributes of a resume were assigned independent of the race and gender. This means that any effects we observe are in fact causal, and the effects related to race are both statistically significant and very large: white applicants had about a 50\

Do you still have doubts lingering in the back of your mind about the validity of this study? Maybe a counterargument about why the standard conclusions from this study may not apply? The article summarizing the results was exceptionally well-written, and it addresses many potential concerns about the study's approach. So if you're feeling skeptical about the conclusions, please find the link below and explore!

Source Bertrand M, Mullainathan S. 2004. "Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination". The American Economic Review 94:4 (991-1013). \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.3386/w9873")}.

Overview

The OpenResume dataset is designed for researchers and practitioners in career trajectory modeling and job-domain machine learning, as described in the IEEE BigData 2024 paper. It includes both anonymized realistic resumes and synthetically generated resumes, offering a comprehensive resource for developing and benchmarking predictive models across a variety of career-related tasks. By employing anonymization and differential privacy techniques, OpenResume ensures that research can be conducted while maintaining privacy. The dataset is available in this repository. Please see the paper for more details: 10.1109/BigData62323.2024.10825519

If you find this paper useful in your research or use this dataset in any publications, projects, tools, or other forms, please cite:

@inproceedings{yamashita2024openresume,

title={{OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets}},

author={Yamashita, Michiharu and Tran, Thanh and Lee, Dongwon},

booktitle={2024 IEEE International Conference on Big Data (BigData)},

year={2024},

organization={IEEE}

}

@inproceedings{yamashita2023james,

title={{JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning}},

author={Yamashita, Michiharu and Shen, Jia Tracy and Tran, Thanh and Ekhtiari, Hamoon and Lee, Dongwon},

booktitle={2023 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},

year={2023},

organization={IEEE}

}

Dataset Card for Resume Dataset

  Dataset Summary





  Context

A collection of Resume Examples taken from livecareer.com for categorizing a given resume into any of the labels defined in the dataset.

  Content

Contains 2400+ Resumes in string as well as PDF format. PDF stored in the data folder differentiated into their respective labels as folders with each resume residing inside the folder in pdf form with filename as the id defined in the csv. Inside the… See the full description on the dataset page: https://huggingface.co/datasets/opensporks/resumes.

We introduce a new experimental paradigm to evaluate employer preferences, called incentivized resume rating (IRR). Employers evaluate resumes they know to be hypothetical in order to be matched with real job seekers, preserving incentives while avoiding the deception necessary in audit studies. We deploy IRR with employers recruiting college seniors from a prestigious school, randomizing human capital characteristics and demographics of hypothetical candidates. We measure both employer preferences for candidates and employer beliefs about the likelihood that candidates will accept job offers, avoiding a typical confound in audit studies. We discuss the costs, benefits, and future applications of this new methodology.

AI scanner to extract data from resumes. Reliable and customizable OCR with API & SDK to convert and validate key candidate information for HR workflows.

Resume NER Training Dataset

This dataset contains training data for Named Entity Recognition (NER) on resume text. It's used to train the yashpwr/resume-ner-bert model.

  Dataset Summary

Task: Token Classification (NER) Language: English Domain: Resume/CV text Size: 22855 examples Format: JSONL with BIO tagging

  Entity Types

The dataset includes the following entity types commonly found in resumes:

PERSON: Names of individuals ORG: Organizations, companies… See the full description on the dataset page: https://huggingface.co/datasets/yashpwr/resume-ner-training-data.

Resume Building Tool Market size was valued at USD 1.6 Billion in 2024 and is projected to reach USD 3.1 Billion by 2032, growing at a CAGR of 8.5% during the forecast period 2026 to 2032. Increased Demand for Professional Resumes: The demand for professionally designed resumes is fueling due to the growing competition in the job market, leading to an increase in the use of resume-building software.Growing Adoption of Online Learning and Career Development: The popularity of online learning platforms and career development courses has made resume-building tools more accessible, as they are frequently integrated with platforms that provide professional advancement resources.Time Efficiency for Job Seekers: Job searchers are increasingly turning to resume-building tools to save time by quickly constructing professional resumes without having to start from zero.Rising Popularity of Freelancing and Gig Economy: The rise of freelancing and gig economy work is fueling the need for resume-building tools, as people strive to properly market their talents to potential clients.Economic Uncertainty and Layoffs: Economic uncertainties and layoffs are driving the resume building tool market. The U.S. Bureau of Labor Statistics forecasted 1.9 million layoffs in Q1 2025; thus many professionals will need to swiftly update or develop new resumes.Technological advancements: The incorporation of AI and machine learning into resume-building tools enables personalized suggestions, keyword optimization, and error correction. These clever features have fueled the desire for more advanced resume-building software, appealing to customers looking for competitive resumes.

Financial overview and grant giving statistics of National Resume Writers Association Ltd

The AI resume builder market is experiencing rapid growth, driven by the increasing need for efficient and effective resume creation in a competitive job market. The market's expansion is fueled by several key factors. Firstly, the rising adoption of AI-powered tools across various industries is streamlining processes and enhancing productivity, making AI resume builders an attractive solution for both individuals and enterprises. Secondly, the increasing volume of job applications and the need for personalized resumes tailored to specific job descriptions are pushing users towards automated solutions offering improved customization and efficiency. Furthermore, the integration of advanced features like AI-powered writing assistance, ATS optimization, and data-driven insights into resume performance provide a clear value proposition for users. While cloud-based solutions dominate due to accessibility and scalability, on-premises options continue to cater to organizations with stringent data security requirements. The market is segmented by application (enterprise and individual) and type (cloud-based and on-premises), with the enterprise segment witnessing significant growth due to the increasing need for efficient talent acquisition and management. Key players such as Teal, Enhancv, and Resume Worded are actively shaping the market with innovative features and expanding market reach, fostering competition and innovation. Geographical analysis reveals strong market penetration in North America and Europe, driven by high digital literacy and adoption of technological advancements. However, emerging markets in Asia-Pacific and the Middle East & Africa show substantial growth potential as digitalization progresses. Despite the significant market opportunities, challenges such as data privacy concerns, the need for continuous algorithm improvement to maintain accuracy, and overcoming the inherent human element in resume creation pose obstacles. Addressing user trust and ensuring ethical considerations in the design and application of AI algorithms are crucial for continued market growth. The industry faces the ongoing challenge of educating users about the benefits and limitations of AI-driven resume builders while maintaining transparency in data usage. Furthermore, the high level of competition among existing players necessitates continuous product innovation and development to retain a competitive edge. Overall, the AI resume builder market holds immense potential for growth, driven by technology advancements, and a growing demand for efficient and effective recruitment solutions. However, addressing ethical considerations, data privacy, and maintaining user trust are key factors that will influence future market trajectories.

Comprehensive dataset containing 2 verified Resume service businesses in Montana, United States with complete contact information, ratings, reviews, and location data.

Dataset

This dataset was created by Wahib Mzali

Released under Data files © Original Authors

Contents

Comprehensive dataset containing 39 verified Resume service businesses in Arizona, United States with complete contact information, ratings, reviews, and location data.

Dataset

This dataset was created by skannan

Released under CC0: Public Domain

Contents

Comprehensive dataset containing 19 verified Resume service businesses in Alabama, United States with complete contact information, ratings, reviews, and location data.

Resume Parsing Software Market size was valued at USD 16.3 Billion in 2023 and is projected to reach USD 43.7 Billion by 2031, growing at a CAGR of 15.1% during the forecasted period 2024 to 2031. Global Resume Parsing Software Market Drivers The market drivers for the Resume Parsing Software Market can be influenced by various factors. These may include:

• Rising Demand for Automated Recruitment Processes: Companies are increasingly adopting automation to streamline their hiring processes, reduce time-to-hire, and enhance efficiency. Resume parsing software allows organizations to quickly filter and categorize large volumes of resumes, saving time and resources. • Increasing Use of Artificial Intelligence (AI) and Machine Learning (ML): AI and ML technologies have improved the accuracy of resume parsing, enabling systems to better understand and categorize resumes based on specific criteria like skills, experience, and qualifications. This technological advancement makes the software more appealing to recruiters and HR teams.

Global Resume Parsing Software Market Restraints Several factors can act as restraints or challenges for the Resume Parsing Software Market. These may include:

• Data Privacy and Security Concerns: Resume parsing software deals with sensitive personal information. The increasing focus on data privacy laws like GDPR and CCPA requires companies to ensure compliance, which can limit the adoption of these tools in regions with stringent regulations. • Inconsistent Formatting and Accuracy Issues: Resumes come in various formats and styles, and resume parsing software may struggle to accurately interpret all variations. This can result in errors, such as incorrect data extraction or incomplete parsing, affecting the reliability of the software.

Raw data-Resume information collection results of Library science, electronic engineering, radiology and linguistics

Anon. [2020]. Pocket statistical Summary = Résumé statistique de poche 2020. Noumea, New Caledonia: Secretariat of the Pacific Community.

The Cover Letter and Resume Services market has witnessed significant evolution over the past few years, driven by the increasing competition among job seekers and the rising necessity for personalized career branding. This market encompasses a range of offerings, including professional resume writing, cover letter