#labormarkets — Public Fediverse posts

Firm Pay, Amenities, and Inequality https://d.repec.org/n?u=RePEc:nbr:nberwo:35149&r=&r=bec
"Non-wage attributes are an important driver of job choice: workers frequently choose lowerpaying offers. Amenity valuations are highly dispersed across firms and approximately orthogonal to wages, so amenities do not offset between-firm pay differences. In money-metric units, the signal variance of amenities is about one-third that of wage premia. Conditional on the wage, high-amenity firms tend to be larger, have lower quit rates, and are more favorably reviewed by employees. Amenity preferences vary across demographic groups. Men and women do not value the same firms equally: the correlation between their firm-specific valuations is 0.239. Women work at firms that pay less. They also work at firms that offer them higher amenity value. Using gender-specific valuations, women do not work at firms that offer them lower overall value. In some specifications, they work at firms that offer more."
#LaborMarkets #wages #ExperimentalEcon #gpg

Firm Pay, Amenities, and Inequality https://d.repec.org/n?u=RePEc:nbr:nberwo:35149&r=&r=bec
"Non-wage attributes are an important driver of job choice: workers frequently choose lowerpaying offers. Amenity valuations are highly dispersed across firms and approximately orthogonal to wages, so amenities do not offset between-firm pay differences. In money-metric units, the signal variance of amenities is about one-third that of wage premia. Conditional on the wage, high-amenity firms tend to be larger, have lower quit rates, and are more favorably reviewed by employees. Amenity preferences vary across demographic groups. Men and women do not value the same firms equally: the correlation between their firm-specific valuations is 0.239. Women work at firms that pay less. They also work at firms that offer them higher amenity value. Using gender-specific valuations, women do not work at firms that offer them lower overall value. In some specifications, they work at firms that offer more."
#LaborMarkets #wages #ExperimentalEcon #gpg

Firm Pay, Amenities, and Inequality https://d.repec.org/n?u=RePEc:nbr:nberwo:35149&r=&r=bec
"Non-wage attributes are an important driver of job choice: workers frequently choose lowerpaying offers. Amenity valuations are highly dispersed across firms and approximately orthogonal to wages, so amenities do not offset between-firm pay differences. In money-metric units, the signal variance of amenities is about one-third that of wage premia. Conditional on the wage, high-amenity firms tend to be larger, have lower quit rates, and are more favorably reviewed by employees. Amenity preferences vary across demographic groups. Men and women do not value the same firms equally: the correlation between their firm-specific valuations is 0.239. Women work at firms that pay less. They also work at firms that offer them higher amenity value. Using gender-specific valuations, women do not work at firms that offer them lower overall value. In some specifications, they work at firms that offer more."
#LaborMarkets #wages #ExperimentalEcon #gpg

Firm Pay, Amenities, and Inequality https://d.repec.org/n?u=RePEc:nbr:nberwo:35149&r=&r=bec
"Non-wage attributes are an important driver of job choice: workers frequently choose lowerpaying offers. Amenity valuations are highly dispersed across firms and approximately orthogonal to wages, so amenities do not offset between-firm pay differences. In money-metric units, the signal variance of amenities is about one-third that of wage premia. Conditional on the wage, high-amenity firms tend to be larger, have lower quit rates, and are more favorably reviewed by employees. Amenity preferences vary across demographic groups. Men and women do not value the same firms equally: the correlation between their firm-specific valuations is 0.239. Women work at firms that pay less. They also work at firms that offer them higher amenity value. Using gender-specific valuations, women do not work at firms that offer them lower overall value. In some specifications, they work at firms that offer more."
#LaborMarkets #wages #ExperimentalEcon #gpg

Firm Pay, Amenities, and Inequality https://d.repec.org/n?u=RePEc:nbr:nberwo:35149&r=&r=bec
"Non-wage attributes are an important driver of job choice: workers frequently choose lowerpaying offers. Amenity valuations are highly dispersed across firms and approximately orthogonal to wages, so amenities do not offset between-firm pay differences. In money-metric units, the signal variance of amenities is about one-third that of wage premia. Conditional on the wage, high-amenity firms tend to be larger, have lower quit rates, and are more favorably reviewed by employees. Amenity preferences vary across demographic groups. Men and women do not value the same firms equally: the correlation between their firm-specific valuations is 0.239. Women work at firms that pay less. They also work at firms that offer them higher amenity value. Using gender-specific valuations, women do not work at firms that offer them lower overall value. In some specifications, they work at firms that offer more."
#LaborMarkets #wages #ExperimentalEcon #gpg

Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
https://docs.iza.org/dp18517.pdf
#AI involvement deters applicants, particularly women, across both pure algorithmic and hybrid human-in-the-loop regimes. This effect is driven by non-competitive candidates; non-competitive women apply least despite receiving the strongest objective evaluations under AI assessment. Competitive men exhibit #overconfidence -driven selection, while competitive women remain resilient and well-calibrated under AI assessment. Notably, randomizing candidate #gender disclosure does not significantly impact application behavior in any evaluation category.
#hiring #llms #algorithmaversion #LaborMarkets #jobtech #ExperimentalEcon

The hidden power keeping wages low
https://www.npr.org/sections/planet-money/2026/04/21/g-s1-118071/the-hidden-power-keeping-wages-low
#Monopsony theory explains how employer dominance suppresses wages by distorting competitive market dynamics. Modern research reveals that market concentration and search frictions grant firms widespread control over pay; most employers have power to keep #wages low because worker options are actually limited. This explains why minimum wage hikes often raise incomes without reducing employment as once predicted. While new rules could raise pay, these fixes are limited by the choices of politicians and companies.
#laborMarkets

A Job I Like or a Job I Can Get: Designing Job #RecommenderSystems Using Field Experiments https://d.repec.org/n?u=RePEc:arx:papers:2603.21699&r=&r=exp
"… welfare-optimal RSs rank vacancies by an expected-surplus index, and shows why rankings based solely on utility, #hiring probabilities, or observed application behavior are generically suboptimal
… Algorithms informed by the model-implied optimal ranking substantially outperform existing approaches and perform close to the welfare-optimal benchmark.

While the joint application-and-hiring probability is not welfare-optimal in theory, it emerges as a strong empirical benchmark in our setting. This result is structural rather than algorithmic: application probabilities are empirically small and remain so even under recommendation rules designed to stimulate applications
… rankings based solely on application behavior are theoretically fragile
… Machine-learning tools can substantially improve matching outcomes, but only when embedded in a framework that defines the economic objective and disciplines behavioral assumptions with experimental evidence. Without such a framework, RSs optimized for observable behaviors may perform well on predictive metrics yet remain misaligned with welfare-relevant outcomes."
#LaborMarkets #jobtech #socialWelfare #ExperimentalEcon

Wall Street Sees AI’s ‘Creative Destruction’ Coming For Entire Companies

(Bloomberg) — A new worry is rippling across the stock market lately: entire businesses, not just their employees,…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Artificialintelligence #AI #AntonKorinek #ArtificialIntelligence #Bloomberg #capitalmarkets #employees #labormarkets #productivity #Technology #videorentalstores
https://www.newsbeep.com/us/506583/

The Work-from-home Wage Premium https://www.frbsf.org/wp-content/uploads/wp2026-02.pdf
"… find that workers who work from home earn higher hourly wages than those who do not.
… premium is driven by selection on unobservable worker characteristics (which could include ability, negotiation skills or bargaining power). Indeed, WFH was more prevalent for workers who already had high hourly wages before the pandemic, and was not associated with higher post-pandemic wage growth.
… in a world with more widespread #WFH, differences in hourly #wages may significantly understate #inequality, as the best-paid workers are also more likely to receive the WFH amenity.
… changes in WFH policies (e.g., through widely debated RTO mandates) could have important implications for the allocation of talent and for aggregate productivity: firms offering WFH disproportionately attract more educated and experienced workers
… stringent #RTO mandates may induce the most productive employees to leave firms that do not offer WFH."
#LaborMarkets

Wage Expectations and Job Search https://d.repec.org/n?u=RePEc:ajk:ajkdps:386&r=&r=eur
"While average misperceptions are relatively small, substantial shares of job seekers display pronounced optimism or pessimism.
… Treated job seekers who were initially strongly optimistic increase their search effort and find jobs more quickly. Conversely, initial pessimists narrow the geographic scope of their search in response to the treatment, which accelerates re-employment—consistent with mitigated spatial search frictions.
… accounting for job seekers’ subjective beliefs is essential when studying search behavior
… suggest that job seekers seem to jointly determine multiple dimensions of their search strategy—including their wage demands, search intensity, and geographic scope. Exogenous changes in one domain can spill over into others
… Both initially optimistic and initially pessimistic job seekers find employment more quickly when holding more accurate beliefs."
#LaborMarkets #jobtech #wageTransparency

The Trust Equation: It’s Not Just Who You Hire, It’s How You Hire https://behavioralscientist.org/the-trust-equation-its-not-just-who-you-hire-its-how-you-hire/
"Talent represents the most valuable asset of any firm, and candidates evaluate employers as rigorously as vice versa. #AI threatens to further depersonalize human interactions. To thrive in an era that threatens to erode human interactions, organizations must create consistently valuable experiences.

The competitive advantage isn’t in fighting harder in the “war for talent” but in building systems that cultivate #trust, performance, and, with it, an employer brand at scale. Every organization claims to put people first. The ones that succeed are those whose processes prove it."
#LaborMarkets #jobtech #hiring

There's Nothing in the Air https://arxiv.org/abs/2510.22294
"the urban wage growth premium: substantially faster wage growth in larger cities
… part of this premium is driven by the firms that choose to sort themselves into bigger cities
… eliminating the job ladder mechanism, the urban wage growth premium falls by 94.1% after accounting for firms and coworkers.
… results challenge the view that cities generate human capital spillovers “in the air,” suggesting instead that urban wage dynamics reflect the #sorting of firms and workers and the pace of job #mobility."
#wages #matching #LaborMarkets

#Signaling in the Age of AI: Evidence from Cover Letters https://d.repec.org/n?u=RePEc:arx:papers:2509.25054&r=&r=lma
"While #AI tools allow freelancers to produce more polished and tailored applications with less effort, our findings suggest that they fundamentally reshape how employers interpret cover letters. The widespread adoption of AI-assisted writing diminishes the informational value of cover letters, weakening their role as a hiring signal.

Workers with weaker pre-AI writing skills saw larger improvements in cover letters, indicating that AI substitutes for workers’ own skills. Although only a minority of applications used the tool, the overall correlation between cover letter tailoring and callbacks fell by 51%, implying that cover letters became less informative signals of worker ability in the age of AI."
#LaborMarkets #jobtech

The Role of Firms and Occupations in Wage Inequality https://d.repec.org/n?u=RePEc:zbw:vfsc25:325461&r=&r=eur
"… find that between-occupation job variance is roughly as important as within-occupation between-firm variation, and that between-occupation sorting is significantly more important than within-occupation between-firm sorting in our context.
… 21% of total log-wage variance can be attributed to between-occupation pay variance and sorting of workers between occupations, while 9% can be attributed to variance within occupations between firms and sorting between workers and firms within occupations.
… suggests that occupation-based factors, such as #heterogeneity in skill prices, are quantitatively more important than firm pay dispersion.
… in higher-wage occupations and larger labor markets firm-level heterogeneity is relatively more important, although the importance of firm-worker sorting does not vary.
… individual-level differences are significantly more important in both higher-wage occupations and larger labor markets."
#LaborMarkets #wages #occupationalClassification #skills

People are using ChatGPT to write their applications; HR is using AI to read them; no one is getting hired.
https://www.theatlantic.com/ideas/archive/2025/09/job-market-hell/684133/
"Online #hiring platforms have made it easier to find an opening but harder to secure one: Applicants send out thousands of AI-crafted résumés, and businesses use #AI to sift through them. What Bumble and Hinge did to the dating market, contemporary human-resources practices have done to the job market. People are swiping like crazy and getting nothing back.

…recommends old-fashioned networking: asking recruiters out for coffee, going to in-person job events, and surveying friends and former employers for leads."
#jobTech #LaborMarkets

Companies are rethinking online job applications, seeking quality over quantity
https://archive.ph/Vn52u#selection-559.0-574.0
"Companies fed up with the low-quality, sometimes fraudulent submissions that flood applicant-tracking systems are reaching back in time for hard-to-hack recruiting methods. Classified ads are just one tack.
Others include: leaning harder on references; making application forms so cumbersome that only serious candidates will complete them; and posting openings on niche job boards instead of the most popular ones.

… All these tools for applicants to get seen are backfiring, forcing me to go to longer and longer lengths to filter out the noise and #AI fraud,"

#jobTech #LaborMarkets #classifieds

Against the Standard https://archiv.ub.uni-heidelberg.de/volltextserver/36879/8/Cubel_Against_dp764_2025.pdf
"… in the absence of feedback, women are less likely than men to benchmark their performance against a standard of excellence. This is inefficient because women who are likely to obtain increased rewards choose a low reward scheme instead.
… When feedback is provided and the standard is set by peers, this gender gap closes. However, the gap re-emerges, and even widens, when the standard of excellence is set by experts.
… If standards are set by experts and committees are perceived as male-dominated, a gender gap will exist in the award of promotions, grants or recognition. Understanding the differential impact of standards and feedback provision can help to design more inclusive competitive processes and bridge gender gaps in labour market outcomes."
#ExperimentalEcon #LaborEconomics #wages #gpg #LaborMarkets

Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Models https://arxiv.org/pdf/2506.10491
"… the estimation of socio-economic parameters shows substantially more bias than subject-based benchmarking. Furthermore, such a setup is closer to a real conversation with an AI assistant. In the era of memory-based AI assistants, the risk of persona-based #LLM bias becomes fundamental. Therefore, we highlight the need for proper debiasing method development and suggest pay gap as one of reliable measures of bias in LLMs
… various forms of #biases when salaries for women are substantially lower than for men, as well as drops in salary values for people of color and of Hispanic origin. In the migrant type category, expatriate salaries tend to be larger, while salaries for refugees are mostly low"

Surprise! These LLMs just replicate the empirical observations of #wages including any wage gaps that may be the result of discrimination that were part of their training data as salary recommendations. These cannot be proper recommendations, of course, they are just a stochastic auto-complete. The biases are real. But you will need tailor-made salary models to generate proper, unbiased salary benchmarks. A #llm is not enough.
#jobtech #LaborMarkets

Fairness Properties of Compensation Schemes https://d.repec.org/n?u=RePEc:ces:ceswps:_11943&r=&r=lma
"… the benefits of providing incentives need to be traded off against unintended side effects due to violation of employees’ #fairness norms
… pay inequality has a strong negative effect on perceived fairness. Controlling for pay #inequality, people consider piece rate schemes fairer than those with a discrete bonus and a tournament design
… if incentive contracts cannot be avoided, they should be designed carefully and motivated with reference to #proceduralFairness"
#LaborMarkets #wages
#ExperimentalEcon

Place-Based Labor Market Inequality https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-40&r=&r=lma
"… substantial #heterogeneity in the degree of labor market #tightness across counties, as measured by the vacancy rate using job postings from Lightcast, and moreover find a close connection between this rate and county income #growth."
#LaborMarkets #LocalLaborMarkets

The résumé is dying, and AI is holding the smoking gun https://arstechnica.com/ai/2025/06/the-resume-is-dying-and-ai-is-holding-the-smoking-gun/
"Some candidates are now taking automation even further, paying for #AI agents that autonomously find jobs and submit applications on their behalf.
… Recruiters report that many of the résumés look suspiciously similar, making it more difficult to identify genuinely qualified or interested candidates.
… Beyond volume, fraud poses an increasing threat
… The frustration has reached a point where AI companies themselves are backing away from their own technology during the #hiring process
… Even when AI screening tools work as intended, they exhibit similar #biases to human recruiters, preferring white male names on résumés—raising legal concerns about #discrimination"
#jobtech #LaborMarkets

Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes https://arxiv.org/pdf/2505.14388
"… achieving gender parity at the shortlist stage does not inherently guarantee #gender parity in final hires, even if hiring managers are gender-unbiased.
… the effectiveness of the equal selection constraint is highly job-specific, driven by the correlation between screener and hiring manager evaluations. Notably, technical roles requiring measurable “hard skills” (e.g., software engineering) tend to exhibit higher correlations, diminishing the effectiveness of equal selection precisely in fields where women are most underrepresented.
… equal predictive accuracy of screening algorithms across genders is insufficient in multistage hiring processes. It is equally important for screening algorithms to maintain gender neutrality concerning their alignment with #hiring managers’ criteria—specifically, algorithms should exhibit no gender differences in their correlation with managerial assessments.
… higher correlations between screeners’ and hiring managers’ assessments not only reduce the effectiveness of equal selection constraints but also negatively affect the expected quality of hires. This suggests a critical design insight: screening algorithms should be constructed to complement, rather than replicate, managerial evaluations."
#LaborMarkets #jobtech #bias

A Bridge Too Far: Signalling Effects of Artificial Intelligence Evaluation of Job Interviews https://d.repec.org/n?u=RePEc:hal:journl:hal-04996541&r=&r=exp
"…investigate whether AI evaluation is interpreted as a positive (high innovativeness) or negative (low people orientation) signal by the job applicant
… #AI evaluation is interpreted more strongly as a signal of how the organisation treats people rather than of how innovative it is.
… removing humans from the selection process appears to be a ‘bridge too far', when it comes to technological advances in the selection process."
#LaborMarkets #jobtech

City size, employer concentration, and wage income inequality https://d.repec.org/n?u=RePEc:hhs:ifauwp:2025_004&r=&r=lma
"… controlling for individual level characteristics, we find that the contribution from firm level factors (firm productivity and rents) to earnings increases with local labor market size
… this average urban wage premium pertains to different segments of the local income distribution, we find that it is larger for workers with higher wages. Our analysis hereby suggests that firm pay premia (firm FEs) play a relatively larger role in explaining upper and top-level income rather than median and lower- level income.
… differences in employer concentration across local industries helps explain income differences across the city size distributionaverage UWP decreases with increasing employer concentration, and vice versa."
#LaborMarkets #wages #heterogeneity

Labor share and market power in European firms https://d.repec.org/n?u=RePEc:pra:mprapa:123442&r=&r=lma
"firms’ product and labor market power, as reflected in product markups and labor markdowns, enable firms to capture a larger share of economic output at the expense of workers.
… empirical analysis shows that markdowns are negative correlated with labor share, as firms reduce employment and suppress #wages below the marginal revenue of labor.
Product markups exhibit a dual effect: they can positively influence the labor share by increasing wages and employment when firms reinvest additional profits into production expansion. … markups indirectly reduce the labor share due to their positive link with labor markdowns. Firms with significant pricing power often suppress wages and limit hiring, diminishing the labor share.
… empirical results reveal the presence of a hump-shaped relationship between product markups and labor share. Initially, as product markups increase, the labor share rises, but this trend reverses when markups reach high levels. This nonlinearity underscores the dual nature of markups: while moderate markups have a direct positive effect on labor share, mitigating labor markdowns, high markups enable firms to exert substantial #monopsony power, diminishing the labor share.
The analysis reveals significant cross-country #heterogeneity in the effects of markups and markdowns on the labor share."
#IndustrialOrganization #LaborMarkets

Overconfidence and gender gaps in career outcomes: insights from a promotion signaling model https://d.repec.org/n?u=RePEc:hhs:ifauwp:2024_021&r=&r=lma
"… male #overconfidence, combined with competitive workplace incentives affects gender equality in the labor market: overconfident workers exert more effort, are more likely to be promoted, and ultimately earn higher wages across job levels despite having lower expected ability conditional on promotion. The higher effort not only increases their chances of promotion, but also contributes to human capital accumulation through learning-by-doing, leading to higher productivity
… overconfidence can be a double-edged sword: while it can lead to higher promotions and wages (serving as a “self-serving bias”), it also imposes higher effort costs and discourages peers
… policies aimed at limiting working hours could help mitigate the effects of overconfidence, potentially reducing the gender gap in career progression and #wages."
#LaborMarkets #BoundedRationality #gpg

via Florian Ederer:
Return-to-office (RTO) mandates lead to abnormally high employee #turnover, especially for female/more senior/more skilled employees.

It also takes significantly longer to fill these job vacancies after #RTO mandates.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5031481
#wfh #LaborMarkets

Jobseekers' beliefs about comparative advantage & (mis)directed search https://ora.ox.ac.uk/objects/uuid:d70f2baf-109c-4bd1-b531-8f0dd4585911/files/s2j62s658t
"Many jobseekers believe they are better at the skill in which they score lower, relative to other jobseekers
…giving them their skill assessment …redirects their search toward jobs that value the skill in which they score higher but does not increase total search effort. It raises earnings & job quality, consistent with inefficient sorting due to limited information."
#LaborMarkets #ExperimentalEcon

Gender Differences in Preferences for Flexible Work Hours: Experimental Evidence from an Online Freelancing Platform https://d.repec.org/n?u=RePEc:iza:izadps:dp17434&r=&r=lab
"…workers value flexibility and the demand is higher for women than for men: Flexible jobs led to a 24 % rise in the number of female applicants and a 12 % rise in the number of male applicants
…an increase in flexibility (but not wage) attracts better female, but not male, candidates"
#LaborMarkets #wages #ExperimentalEcon

Worker Beliefs About Outside Options https://microeconomicinsights.org/worker-beliefs-about-outside-options/
"…anchoring & misperceptions about the wage distribution can be a source of #LaborMarkets imperfections
…in standard models, workers are assumed to have perfect information about the wage distribution, their position therein, and hence their outside options
The presence of misperceptions also gives rise to distinct policy remedies, such as #PayTransparency mandates"
#BoundedRationality #wages

Minimum Wages in Concentrated #LaborMarkets https://d.repec.org/n?u=RePEc:iza:izadps:dp17357&r=&r=lma
"Labor Market concentration turns out substantial in Germany …higher concentration reduces #wages & employment, reflecting monopsonistic conduct of firms. Sectoral minimum wages lead to negative employment effects in more competitive labor markets
…empirical support to the #monopsony argument, implying that conventional minimum wage effects on employment conceal #heterogeneity across market forms

Companies have touted new #AI technology that allows users to apply to thousands of jobs per day, flooding firms with résumés https://www.nbcnews.com/tech/innovation/ai-making-job-applications-easier-creating-another-problem-rcna179683
"…who screens and hires job applicants for a living, emphasizes the need to use AI cautiously to helpand not harm—your chances at getting noticed by a company …recommends that applicants always review generated responses, noting that “AI misses things, too.”
Same is true for the firm!
#jobtech #LaborMarkets

Sorting and wage premiums in immoral work https://d.repec.org/n?u=RePEc:kud:kucebi:2412&r=&r=exp
jobs perceived as immoral command higher #wages, & individuals with lower concerns for #morality are more likely to accept such jobs
… corroborated by administrative data showing that industries perceived as immoral pay higher wages, even after controlling for various worker and industry characteristics
#ExperimentalEcon #LaborMarkets #ethics

…to find the best workers, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality)
…modern #hiring algorithms are designed solely for exploitation
…algorithm that values exploration… improves the quality of candidates selected for an interview, while also increasing demographic diversity
https://arxiv.org/pdf/2411.03616
#LaborMarkets #jobtech

Recovering Overlooked Information in Categorical Variables with LLMs: An Application to Labor Market Mismatch
https://web.sas.upenn.edu/hfang/files/2024/04/NBER_w32327-5144bf32f205bbda.pdf
"…#LLM's match quality measure is positively correlated with traditional measures
…when gender information is disclosed to the #LLM, the model deems females better suited for traditionally female-dominated roles"
#LaborMarkets
#discrimination
#matching
#jobTech
#AIEthics

Mitigating Age Biases in Resume Screening AI Models
"…trained an #AI model & applied #bias correction techniques …to correct for biases based on race, gender, & age. We analyzed the effectiveness of these tools in mitigating different types of bias in job #hiring algorithms, explored why age may be more challenging to eliminate than other forms of bias"
https://journals.flvc.org/FLAIRS/article/view/133236/137923
Interesting paper with a generous reference to https://economicscience.net/publications/2009-more/
#Discrimination #ageism
#JobTech #LaborMarkets

If you are interested in #JobTech, #LaborMarkets, and have some relevant #DataScience and #research experience, maybe this #job is for you: Data Science Team Lead http://www.stepstone.de/5/firmen-vakanzen.html?&OfferId=9091204&cid=B2C_CLC_SYS8_Listing

Other locations may be possible, too. I am in Berlin.

#vacancy