Picking Machine Operator Helper
541 005 ₽ (RUB)/yr
260,10 ₽ (RUB)/hr
6 708 ₽ (RUB)/yr
The average picking machine operator helper gross salary in Nizhny Novgorod, Russian Federation is 541 005 ₽ or an equivalent hourly rate of 260 ₽. This is 19% higher (+87 356 ₽) than the average picking machine operator helper salary in Russian Federation. In addition, they earn an average bonus of 6 708 ₽. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Nizhny Novgorod, Russian Federation. An entry level picking machine operator helper (1-3 years of experience) earns an average salary of 423 906 ₽. On the other end, a senior level picking machine operator helper (8+ years of experience) earns an average salary of 646 429 ₽.
This page is a promotion for SalaryExpert’s Assessor Platform and is not intended for professional use.
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ERI’s compensation data are based on salary surveys conducted and researched by ERI. Cost of labor data in the Assessor Series are based on actual housing sales data from commercially available sources, plus rental rates, gasoline prices, consumables, medical care premium costs, property taxes, effective income tax rates, etc.
644 160 ₽ (RUB)
19 %
Based on our compensation data, the estimated salary potential for Picking Machine Operator Helper will increase 19 % over 5 years.
This chart displays the highest level of education for:
Picking Machine Operator Helper, the majority at 100% with less than hss.
The cost of living in Nizhny Novgorod, Russian Federation is 7% less than the average cost of living in Russian Federation. Cost of living is calculated based on accumulating the cost of food, transportation, health services, rent, utilities, taxes, and miscellaneous.
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This page is a promotion for SalaryExpert’s Assessor Platform and is not intended for professional use.
Professionals should subscribe to SalaryExpert’s Assessor Platform.
ERI’s compensation data are based on salary surveys conducted and researched by ERI. Cost of labor data in the Assessor Series are based on actual housing sales data from commercially available sources, plus rental rates, gasoline prices, consumables, medical care premium costs, property taxes, effective income tax rates, etc.