Recruitment Websites Research Articles

Job Matching Analysis Based on Text Mining and Multicriteria Decision-Making

The postmanagement of an enterprise is to rationally and scientifically arrange the talents who meet the postrequirements and are suitable for the development of the enterprise in their suitable posts through testing, selection, and employment. The job recruitment information published on the recruitment website is the data that best reflects the market demand for data analysis talents. However, as online recruitment information is mostly presented in the form of text, this paper explores the information contained in the recruitment information through text mining technology, which has reference value for data analysis job seekers. Based on text mining and multicriteria decision-making, this paper studies job matching. The technical jobs under text mining are mainly distributed in the Internet industry, and the number of recruits accounts for 66.45%. Other industries have less demand for technical data analysis, all of which are below 15%. However, the market of business data analysis is scattered and extensive, and education still occupies the largest demand provider, accounting for 30.53%. However, real estate and media are closely followed, accounting for 24.28% and 22.34%, respectively. Besides these three industries, compared with technical talents, service industries will also need business data analysis talents more. In the stage of data collection, through the data analysis of the recruitment website, the job recruitment information is crawled, as well as other job information such as job salary, job qualifications, work experience requirements, and the number of recruits. This paper carries out the data collection stage and crawls the job recruitment information through the data analysis of the recruitment website. And, other position information, such as position salary, position qualification, and work experience requirements, as well as the number of recruits. The research of this paper can help the data released by the recruitment website to analyze the recruitment information and quickly reflect the market demand data for data analysis talents.

Do reasoned action approach variables mediate relationships between demographics and cervical cancer screening intentions or behaviour? An online study of women from the UK

Objective: In the UK, approximately 3,200 women are diagnosed with cervical cancer each year. Regular screening is one of the best ways to prevent cervical cancer from developing, yet screening rates are declining and vary by sociodemographic variables. The present stratified online study aimed to investigate relationships between sociodemographic factors and screening intentions and past behaviour. Reasoned Action Approach (RAA) variables were assessed as potential mediators. MethodsIn total, 500 women living in the UK were recruited via an online research recruitment website to an online cross-sectional survey. Participant recruitment was stratified by age, socioeconomic status and ethnicity to ensure adequate representation of each strata. Participants completed measures on RAA variables (affective attitudes, cognitive attitudes, injunctive norms, descriptive norms, capability, autonomy, and intention) as well as screening past behaviour. ResultsAmong the demographic variables, age, ethnicity and deprivation were significantly related to screening intention. Younger women, those from less deprived areas, along with white women were more likely to report higher intentions to attend screening. Past behaviour was significantly negatively predicted by deprivation only, indicating that individuals from less deprived areas were more likely to be up to date with their screening. Both intention and past behaviour were significantly positively correlated with all RAA variables. Capability and cognitive attitude partially mediated the relationship between age and intention and fully mediated the relationship between ethnicity and intention. Capability fully mediated the relationships between deprivation and intention and between deprivation and past behaviour. ConclusionIntentions toward attending cervical cancer screening are related to age, ethnicity, and level of deprivation, with older women and those from areas of greater deprivation and ethnic minority groups reporting lower intentions. Capability (confidence engaging in cervical screening) and cognitive attitudes (how useful/beneficial screening is seen to be) are key variables to target to promote cervical screening attendance and reduce potential inequalities.

Analysis of Human Capital Social Network Model Based on Industry Distribution

As the most critical resource to promote the continuous growth of economic volume, human resources are indispensable to the development of the industry. With the development of market economy and the improvement of people’s pursuit of quality of life, the time of talent flow is increasing, which forms the social network of human capital. As a new stock, human capital social network can have an impact on individual thoughts or behaviors. This paper with a recruitment website workers repeatedly changes employment resume as the research object and uses literature research and empirical analysis based on human capital social network model analysis for the industry distribution of employment analysis and discussion. The results show that the IT internet industry in labor cross-industry network has high resource control and information advantage and has large correlation with other industries; traditional industry is limited; construction, leasing, and business services, electricity, heat, coal, and water flow frequency are the lowest; and the network structure is more stable.

A Cost, Time, and Demographic Analysis of Participant Recruitment and Urine Sample Collection Through Social Media Optimization.

Clinical research can be expensive and time consuming due to high associated costs and/or duration of the study. We hypothesized that urine sample collection using online recruitment and engagement of research participants via social medial has the potential to reach a large population in a small timeframe, at a reasonable cost. We performed a retrospective cost analysis of a cohort study comparing cost per sample and time per sample for both online and clinically recruited participants for urine sample collection. During this time, cost data were collected based on study associated costs from invoices and budget spreadsheets. The data were subsequently analyzed using descriptive statistics. Each sample collection kit contained 3 urine cups, 1 for the disease sample and 2 for control samples. Out of the 3,576 (1,192 disease + 2,384 control) total sample cups mailed, 1,254 (695 control) samples were returned. Comparatively, the 2 clinical sites collected 305 samples. Although the initial startup cost of online recruitment was higher, cost per sample for online recruited was found to be $81.45 compared to $398.14 for clinic sample. We conducted a nationwide, contactless, urine sample collection through online recruitment in the midst of the COVID-19 pandemic. Results were compared with the samples collected in the clinical setting. Online recruitment can be utilized to collect urine samples rapidly, efficiently, and at a cost per sample that was 20% of an in-person clinic, and without risk of COVID-19 exposure.

Research on Data Analysis and Visualization of Recruitment Positions Based on Text Mining

With the rapid development of the Internet and the impact of COVID-19, online recruitment has gradually become the mainstream form of recruitment. However, existing online recruitment platforms fail to fully combine the job seekers’ demands for salary, region, benefits, and other aspects, which cloud not display the information related to recruitment positions in a multidimensional way. To solve this problem, this paper firstly uses a web crawler to collect job information from recruitment websites based on keywords retrieved by users, then extracts job information using regular expressions, and cleans and processes the extracted job information using third-party libraries such as Pandas and NumPy. Finally, through the probabilistic theme model of text mining, the topic model of job description content in the recruitment information is modeled. Combining with the django development framework and related visualization technology, the relationship among education requirement, experience requirement, job location, salary, and other aspects in the recruitment information is visually displayed in a multidimensional way. At the same time, the GM model is used to realize the gray prediction of the number of employment personnel in related industries, which provides employment reference for the majority of job seekers and enterprises.

Aligning the American Health Information Management Association Entry-level Curricula Competencies and Career Map With Industry Job Postings: Cross-sectional Study

BackgroundThe field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA’s HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA’s career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands.ObjectiveThe primary aim of this study is to analyze HIM job postings on a US national job recruiting website to determine whether the job postings align with recognized HIM domains, while the secondary aim is to evaluate the AHIMA career map to determine whether it aligns with the job postings.MethodsA national job recruitment website was mined electronically (web scraping) using the search term “health information management.” This cross-sectional inquiry evaluated job advertisements during a 2-week period in 2021. After the exclusion criteria, 691 job postings were analyzed. Data were evaluated with descriptive statistics and natural language processing (NLP). Soft cosine measures (SCM) were used to determine correlations between job postings and the AHIMA career map, curricular competencies, and curricular considerations. ANOVA was used to determine statistical significance.ResultsOf all the job postings, 29% (140/691) were in the Southeast, followed by the Midwest (140/691, 20%), West (131/691,19%), Northeast (94/691, 14%), and Southwest (73/691, 11%). The educational levels requested were evenly distributed between high school diploma (219/691, 31.7%), associate degree (269/691, 38.6%), or bachelor’s degree (225/691, 32.5%). A master’s degree was requested in only 8% (52/691) of the postings, with 72% (42/58) preferring one and 28% (16/58) requiring one. A Registered Health Information Technologist (RHIT) credential was the most commonly requested (207/691, 29.9%) in job postings, followed by Registered Health Information Administrator (RHIA; 180/691, 26%) credential. SCM scores were significantly higher in the informatics category compared to the coding and revenue cycle (P=.006) and data analytics categories (P<.001) but not significantly different from the information governance category (P=.85). The coding and revenue cycle category had a significantly higher SCM score compared to the data analytics category (P<.001). Additionally, the information governance category was significantly higher than the data analytics category (P<.001). SCM scores were significantly different between each competency category, except there were no differences in the average SCM score between the information protection and revenue cycle management categories (P=.96) and the information protection and data structure, content, and information governance categories (P=.31).ConclusionsIndustry job postings primarily sought degrees, with a master’s degree a distant fourth. NLP analysis of job postings suggested that the correlation between the informatics category and job postings was higher than that of the coding, revenue cycle, and data analytics categories.

Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration

With millions of students/employees browsing course information and job postings every day, the need for accurate, effective, meaningful, and transparent course and job recommender systems is more evident than ever. The current recommendation research has attracted wide attention in the academic and industrial areas. However, existing studies primarily focus on content analysis and user feature extraction of courses or jobs and fail to investigate the problem of cross-domain data integration between career and education. At the same time, it also fails to fully utilize the relations between courses, skills, and jobs, which helps to improve the accuracy of the recommendation. Therefore, this study aims to propose a novel cross-domain recommendation model that can help students/employees search for suitable courses and jobs. Employing a heterogeneous graph and community detection algorithm, this study presents the Graph-Community-Enabled (GCE) model that merges course profiles and recruiting information data. Specifically, to address the skill difference between occupation and curriculum, the skill community calculated by the community detection algorithm is used to connect curriculum and job information. Then, the innovative heterogeneous graph approach and the random walk algorithm enable cross-domain information recommendation. The proposed model is evaluated on real job datasets from recruitment websites and the course datasets from MOOCs and higher education. Experiments show that the model is obviously superior to the classical baselines. The approach described can be replicated in a variety of education/career situations.

0365 Evaluation of Sleep Medicine Fellowship Program Websites

IntroductionFellowship program websites often serve as the initial resource applicants use to learn about programs. Websites have likely become even more important due to social distancing mandates related to the on-going Covid-19 pandemic. In this study, we evaluated the websites of sleep medicine fellowship programs and analyzed the comprehensiveness of their content.MethodsSleep medicine fellowship programs in the United States (US) for the 2021 match cycle were identified using the Electronic Residency Application Service (ERAS) directory and the Fellowship and Residency Electronic Interactive database (FREIDA). Twenty-two prespecified website content criteria related to education, recruitment, and compensation were evaluated. Programs’ website comprehensiveness was compared based on geographic location (Northeast/Midwest/South/West); type of programs (Community/University); programs matching status (Complete/Partial or No matching status); core specialty (Internal medicine/Other specialties); and program size (based on number of sleep fellows).ResultsA total of 78 US sleep fellowship program’ websites were evaluated. Most (80.8%) had a direct functional link to ERAS or FREIDA websites. The percentage of sleep medicine fellowship program’ websites reporting each of the twenty-two-criterion was highly variable (range: 2.6%-98.7%). The percentage of overall website comprehensiveness among sleep medicine fellowship programs was 56.8%±16.5% (range:13.6%-90.9%). There was a significantly higher educational website content comprehensiveness for the Internal medicine compared to other specialties-based sleep programs (p = 0.002). There were no significant association between the overall, educational, recruitment, and compensation website content comprehensiveness of sleep programs and their US region location, type of affiliation, matching status, or program size.ConclusionWebsite content comprehensiveness amongst sleep fellowship programs in the US is variable with a lower educational content on website pages of non-internal medicine-based sleep programs. Improvement in website content of sleep medicine programs is a potentially easy way for programs to improve fellow recruitment, and more importantly, allow prospective sleep fellow applicants to make a more informed decision with regards to program selection.Support (If Any)This study was not funded.

Software Requirements Engineer’s Ability Assessment Method Based on Empirical Software Engineering

With the expansion of the scale and complexity of modern software systems, the failure rate of software projects remains high. One of the main reasons for the failure of software projects is the defects in processing software requirements. This paper proposes a software requirements engineer’s ability assessment method based on empirical software engineering to measure the matching degree between the software requirements engineer’s ability and industry expectations. First, collect the recruitment information of software requirements engineers from mainstream recruitment websites. Through natural language processing, the words related to the abilities of the software requirements engineer are counted. These words are summarized in the requirements acquisition, requirements analysis, and other SRE activities, then the industry expectations for various abilities are obtained. Later on, the authors collect the teaching settings of representative SRE courses, reflecting the software requirements engineer’s ability to learn the course. After that, this article defines the ratio of the industry expectation weight to the weight of each SRE activity in teaching as the software requirements engineer’s ability coefficient, which can intuitively reflect the matching degree between the software requirements engineer’s ability and industry expectations. Finally, take the national first-class undergraduate course “SRE” of Jinling Institute of Technology as an example to verify the method’s practicality to a certain extent.

Older adults’ willingness to consider deprescribing when experiencing hyperpolypharmacy

BackgroundOlder adults experiencing hyperpolypharmacy (use of 10 or more medications) are at an increased risk of cognitive impairment and functional decline. Deprescribing, where medications are stopped or tapered, is one strategy to mitigate risks. ObjectivesThe primary objective of our study was to use a card sorting activity to explore how older adults experiencing hyperpolypharmacy make hypothetical deprescribing decisions. MethodsWe recruited participants using our institutional research recruitment website between February and November 2020. Participant spoke with a research assistant to create a medication list and then completed an interview using card sorting activity to demonstrate how they would make hypothetical decisions about continuing or deprescribing their medications. Data from the card sorting activities and interviews were organized via Excel (Microsoft Corporation). We used the Pharmacy Quality Alliance Medication Therapy Problems Categories Framework to analyze participant’s reasons for considering deprescribing. The study was deemed exempt by the institutional review board. ResultsAmong the 26 participants, 14 (54%) identified as female, 19 (73%) were white, and 24 (92%) reported good or very good health. Participants reported a total of 405 medications (average 16, range 10-30). A total of 19 participants (73%) were interested in deprescribing 94 medications (23%), including stopping 68 medications (72%) and lowering the dose or frequency of 26 medications (28%). Common rationales for wanting to stop a medication included perceived lack of indication (n = 30, 32%), adherence (general preference to not take the medication) (n = 20, 21%), lack of effectiveness (n = 17, 18%), and concerns about safety (n = 14, 15%). We were unable to categorize 13 rationales (14%). ConclusionMost older adults experiencing hyperpolypharmacy were willing to consider deprescribing at least one medication. Future research is needed to identify whether a card sorting medication reflection activity can be used to provoke conversations about deprescribing between patients and primary care providers.

Strategies for Enhancing Career Education of Non-General Language University Students under the Orientation of Market Demand--Taking Central and Eastern European Languages as Examples

In the post-epidemic era, non-general language graduates are facing greater pressure in employment, and students are confused about their career development prospects, meaning the education work based on career development urgently needs to be improved. Using Python programming and manual search as its methods, this paper searches and analyzes the recruitment requirements from 10 key recruitment websites and summarizes the current situation and characteristics of social demand for Central and Eastern European(CEE) languages professionals from different work units. Under the guidance of market demand, four strategies are proposed in a targeted manner to advance the educational level, to broaden the coverage, to intervene with human warmth and to take vigorous steps in career education for CEE languages majors.

Impact of Online Recruitment on Organizational Attractiveness: An Experimental Study in the Context of Pakistan

The purpose of this study is to explore the aesthetical features of corporate recruitment websites, including the website design, style, and color, and to investigate their impact on organizational attractiveness and applicants' intention to apply. By doing so, the study also examines the effects of corporate websites on applicants' perception of the corporate culture and measures the mediating impact of perceptions of organizational culture on organizational attractiveness and applicants' intention to apply for the jobs in the context of Pakistan. The study is based on a laboratory experiment with three hundred undergraduate business students conducted at a university computer lab. Correlation, Independent T-test, One-way ANOVA, and Simple Linear Regression were applied to analyze variables using SPSS. The findings of this study support the hypothesis that website features have a significant positive impact on applicants' perceived organizational attractiveness. However, the website's design does not seem to impact applicants' intentions to apply for the job substantially. The regression analysis results also revealed that the perceived organizational culture partially mediates the relationship between website features and organizational attractiveness and fully mediates the relationship between website features and intentions to Apply.

The Gender-Biased Wording of Recruitment Advertisements in Iraq

This research studies the nature and the scope of gender discrimination in job advertisements across Iraq’s various business sectors, segments, and hierarchical levels. It also attempts to understand the correlation between the language of the vacancy announcements and the level of the gender discriminatory content in them. This study is conducted with the hope of contributing to gender equality at the workplace in Iraq and the wider region. The current study adopts a content coding and analysis method that depends on the analysis of job advertisements (n=1015) by organizations operating in Iraq published in the leading recruitment websites and social media pages for a period of about four consecutive months from June to October 2017. The analysis is conducted using descriptive statistics and tested using simple cross tabulation method. Although the topic has been studied in various countries and contexts, it lacks academic attention in the Middle East, which can be seen as a unique area for research. Also, this research is the first attempt, as far as we are aware of, to comprehend the correlation between the choice of language (English, Arabic or Kurdish) and gender-biased wording of vacancy announcements. Understanding the relationship between language and gender discrimination in job advertisements might as well unveil a new area of study and aid in the quest for gender equality in the Iraqi workplace. This paper provides scientific evidence that more than 41% of all job advertisements in Iraq commit gender discrimination. The majority of them indirectly favor male candidates. Additionally, the nature of the vacancies for which women are preferred is different from the ones that target men. The adverts generally try to segregate women into non-managerial and administrative jobs. The study also infers a statistically significant correlation between the language of the advertisement and gender discrimination rate.

City size and employment dynamics in China: Evidence from recruitment website data

City size and employment dynamics in China: Evidence from recruitment website data

Financial incentives for bowel cancer screening: Results from a mixed methods study in the United Kingdom.

The purpose of bowel cancer screening is to test for signs of cancer before symptoms develop. Financial incentives are one potential method to increase participation rates. Few studies have tested incentives in relation to bowel screening in the United Kingdom (UK). The current research explored reactions to different financial incentives to participate in population-level bowel cancer screening in a UK sample. An online mixed methods study. Recruitment was via a study recruitment website (https://prolific.ac/). 499 participants (aged 60-74 years) completed a survey on invitations for population-level bowel cancer screening using different levels of financial incentives. Respondents were generally positive about the use of financial incentives. A £10 voucher was most frequently selected as the appropriate amount to incentivise screening participation. The current invitation method with no voucher was judged to be most acceptable but suggested to produce the lowest likelihood of others participating. Offering a £10 voucher that the NHS would not be charged for if not used was the second most acceptable invitation method. There were few differences between invitation methods on own perceived likelihood of participation in bowel screening. Offering a £10 voucher was seen as leading to the greatest likelihood of others participating in bowel screening. Findings were largely unaffected by participant demographics. The use of small financial incentives to increase bowel cancer screening uptake was generally well received. Impacts of incentives on actual bowel screening rates in UK samples need to be established in the light of the current findings.

A/B design testing of a clinical trial recruitment website: A pilot study to enhance the enrollment of older adults

IntroductionOnline tools are increasingly utilized in clinical trial recruitment. A/B testing is an effective technology used in political campaigns and commercial marketing to improve contributions or sales. However, to our knowledge, A/B has not been described in the context of clinical trial recruitment. MethodsTwo A/B testing experiments were implemented on the recruitment website of the Study To Understand Fall Reduction and Vitamin D in You (STURDY), a response-adaptive, two-stage, randomized controlled trial. Commercial A/B platforms randomized web-users to different versions of the trial's website landing page; Experiment 1 included two infographic versions and Experiment 2 included three video versions. We compared web-user engagement metrics between each version and the original landing page. We determined the effect of each version compared to the original landing page on the likelihood of a web-user to (1) request more information about the trial, (2) complete a screening visit, or (3) enroll in the trial. ResultsA total of 2605 and 374 web-users visited the trial's website during Experiment 1 and 2, respectively. Response to the online interest form significantly differed by infographic version in Experiment 1. The number of individuals who engaged with website content and pages significantly differed by video in Experiment 2. ConclusionIn a pilot study implementing A/B testing of a clinical trial recruitment website, different versions of the website led to differences in web-user engagement and interest in the trial. A/B testing tools offer a promising approach to test the effectiveness of clinical trial recruitment materials and to optimize recruitment campaigns. Clinical trial registrationThis trial is registered at ClinicalTrials.gov. The trial registration number is NCT02166333. The URL is: https://clinicaltrials.gov/ct2/show/NCT02166333Trial Registration Number: NCT02166333Trial Register: ClinicalTrials.gov

Online Recruitment Website is Significantly Influence the Job-Seekers to Apply Job Online

The Malaysian Employer Federation or MEF (2015) are expected to have around 350,000 jobs offered in the various sector that relevant in the area of trade, restaurant and accommodation. The previous record showing some growth on the hire in the industrial segment by 58,000 jobs. These are the two key areas that contribute to the employment activities in the market. According to Jackson and Shuler (2003), recruitment involves looking and finding competent candidates that best fit for an organization with regards to huge demand from the market imperatively for Human Resource Management (HRM). Therefore, this research is to evaluate the factors influencing job-seekers’ intention to use online recruitment websites, with the hope that the recruitment portals and organizations that want to use online recruitment websites for their recruiting purposes will be able to make use of the information gathered from the study to enhance user experience when using online recruitment websites for job-seeking activities. For the purpose of this study, information pertaining to users’ attitude intended action to use, continue using, and recommending others to use online recruitment websites is gathered. A total of 208 respondents are required to specify the degree to which they strongly agree or strongly disagree. Three statements involving to Intention to Use online recruitment websites using a 5-point Likert scale (1=strongly disagree, 5=strongly agree). Analysis of data using SPSS. The outcome showed there is a significant relation between DV and IVs.

Data Science Talents Mining from Online Recruitment Market in China Based on Data Mining Technique

As the country implements the big data strategy and accelerates the construction of a digital China, data science has entered a new and dynamic era, and the demand for data science talents in all walks of life is increasing. Many talent training departments have added undergraduates or degrees to data science talents, but it is still unclear whether they can meet social and economic development needs. This article aims to improve the quality and adaptability of data science talent training and conduct an in-depth analysis of the demand for data science talents. The technology used in this article is data mining technology. The data information of data science talents is crawled out of the demand information of data science talents on the recruitment website. The core content of network relationship visualization is proposed and analyzed through machine learning methods and text subject word extraction models. Achieve a comprehensive exploration of the demand for data science talents and provide a reference for talent training units to formulate data science talent training models.

Learning from remote decentralised clinical trial experiences: A qualitative analysis of interviews with trial personnel, patient representatives and other stakeholders.

AimsThe aim of the study was to identify actionable learning points from stakeholders in remote decentralised clinical trials (RDCTs) to inform their future design and conduct.MethodsSemistructured interviews were carried out with a purposive sample of stakeholders, including senior managers, trial managers, technology experts, principal investigators, clinical investigators, research scientists, research nurses, vendors, patient representatives and project assistants. The interview data were coded using a thematic approach, identifying similarities, differences and clustering to generate descriptive themes. Further refinement of themes was guided by empirical phenomenology, grounding explanation in the meanings that interviewees gave to their experiences.ResultsForty‐eight stakeholders were interviewed. Actionable learning points were generated from the thematic analysis. Patient involvement and participant engagement were seen as critical to the success of RDCTs where in‐person contact is minimal or nonexistent. Involving patients in identifying the research question, creating recruitment materials, apps and websites, and providing ongoing feedback to trial participants were regarded as facilitating recruitment and engagement. Building strong relationships early with trial partners was thought to support RDCT conduct. Multiple modes of capturing information, including patient‐reported outcomes (PROs) and routinely collected data, were felt to contribute to data completeness. However, RDCTs may transfer trial activity burden onto participants and remote‐working research staff, therefore additional support may be needed.ConclusionRDCTs will continue to face challenges in implementing novel technologies. However, maximising patient and partner involvement, reducing participant and staff burden, and simplifying how participants and staff interact with the RDCT may facilitate their implementation.

Analysis of big data job requirements based on K-means text clustering in China.

This paper aims to understand the characteristics of domestic big data jobs requirements through k-means text clustering, help enterprises, and employees to identify big data talents, and promote the further development of big data-related research. Firstly, the crawler software is used to crawl the recruitment information about "big data" on the zhaopin.com recruitment website. Then, Jieba word segmentation and K-means text clustering are used to cluster big data recruitment positions, and the number of clustering was determined by the average sum of squares within the group. Finally, big data jobs are divided into 10 categories, and the urban distribution, salary level, education requirements, and experience requirements of big data jobs are discussed and analyzed from the perspectives of the overall data set and clustering results, to clarify the characteristics of big data job demands. The analysis results show that the job demands of big data are mainly distributed in first-tier cities and new first-tier cities. Enterprises are more inclined to job seekers with a college degree or bachelor's degree and more than one year's relevant experience. There are wage differences among different types of jobs. The higher the position, the higher the requirement for education and experience will be.