Highly prevalent in poultry, Campylobacter is a foodborne pathogen which remains the primary cause of enteritis in humans. Several studies have determined prevalence and contamination level of this pathogen throughout the food chain. However it is generally performed in a deterministic way without considering heterogeneity of contamination level. The purpose of this study was to quantify, using probabilistic tools, the contamination level of Campylobacter spp. on chicken carcasses after air-chilling step in several slaughterhouses in France. From a dataset (530 data) containing censored data (concentration < 10 CFU/g), several factors were considered, including the month of sampling, the farming method (standard vs certified) and the sampling area (neck vs leg). All probabilistic analyses were performed in R using fitdistrplus, mc2d and nada packages. The uncertainty (i.e. error) generated by the presence of censored data was small (ca 1 log10) in comparison to the variability (i.e. heterogeneity) of contamination level (3 log10 or more), strengthening the probabilistic analysis and facilitating result interpretation. The sampling period and sampling area (neck/leg) had a significant effect on Campylobacter contamination level. More precisely, two “seasons” were distinguished: one from January to May, another one from June to December. During the June-to-December season, the mean Campylobacter concentration was estimated to 2.6 [2.4; 2.8] log10 (CFU/g) and 1.8 [1.5; 2.0] log10 (CFU/g) for neck and leg, respectively. The probability of having > 1000 CFU/g (higher limit of European microbial criterion) was estimated to 35.3% and 12.6%, for neck and leg, respectively. In contrast, during January-to-May season, the mean contamination level was estimated to 1.0 [0.6; 1.3] log10 (CFU/g) and 0.6 [0.3; 0.9] log10 (CFU/g) for neck and leg, respectively. The probability of having > 1000 CFU/g was estimated to 13.5% and 2.0% for neck and leg, respectively. An accurate quantification of contamination level enables industrials to better adapt their processing and hygiene practices. These results will also help in refining exposure assessment models.