Scientist Machine Learning
2 291 982 ₽ (RUB)/yr
1 101,91 ₽ (RUB)/hr
105 202 ₽ (RUB)/yr
The average scientist machine learning gross salary in Nizhny Novgorod, Russian Federation is 2 291 982 ₽ or an equivalent hourly rate of 1 102 ₽. This is 6% higher (+121 096 ₽) than the average scientist machine learning salary in Russian Federation. In addition, they earn an average bonus of 105 202 ₽. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Nizhny Novgorod, Russian Federation. An entry level scientist machine learning (1-3 years of experience) earns an average salary of 1 621 484 ₽. On the other end, a senior level scientist machine learning (8+ years of experience) earns an average salary of 2 877 664 ₽.
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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.
2 729 001 ₽ (RUB)
19 %
Based on our compensation data, the estimated salary potential for Scientist Machine Learning will increase 19 % over 5 years.
This chart displays the highest level of education for:
Scientist Machine Learning, the majority at 100% with bachelors.
Typical Field of Study: Computer Programming, Specific Applications
See how education can impact your salaryThe 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.