As in previous years, this year we ran the MDLI community’s annual survey, so as to map various trends among those who work in the data science and machine learning fields. This year, an exceptional number of respondents completed our annual survey – 1,250 people – a respectable achievement by all counts. Omri Goldstein, an algorithm developer, data scientist, and creator of the “Data-driven” blog analyzed the survey’s findings. We used his analysis to generate the MDLI community’s 2021 annual payroll report. We also developed a dedicated salary calculator for data professionals in Israel.
In the coming weeks, several additional reports highlighting the survey’s results will be uploaded to the website. They will cover topics such as the following: how we built our new salary calculator, the status of gender pay gaps in the market, and the analysis of various roles and their associated responsibilities. The full payroll report is the first publication on the matter to be released, and it can be viewed, in its entirety, below.
In this report, we will segment the salary data according to position, education, seniority, age, experience, gender, and other metrics. When analyzing the influence of various responses on the salary, we are, of course, exposed to the influence of intervening variables. For example, the average salary mentioned in the survey was 38,500 NIS per month for those working in Tel Aviv, as opposed to 26,600 NIS per month for those working in Jerusalem. These findings are likely to be useful to people considering a move from the capital to Tel Aviv (or vice versa. That being said, one cannot expect a roughly 12,000 NIS salary jump based on relocation alone. Perhaps the available positions are different in Tel Aviv (variety and quantity)? Or, maybe the average education level is the cause? There is no end to lines that can be drawn and the environmental links that can be identified. In this report, we will rely on intersections between 2-3 variables at a time and will present the average salary, as well as other statistical metrics for each category. This, so as to learn as much as possible about the distribution as a whole.
Note: No singular data points will be presented, so as to fastidiously protect the privacy of each and every respondent (and our ability to learn from the conclusions). Data presented will always be an aggregation of at least 10 samples.
The Salary Calculator
Before we get started, we’d like to circle back to the MDLI community’s new salary calculator, which we will be launching for the first time this year, based on insights from the survey. Attempts to find salary information on high-tech professions generally lead to various recruitment company’s salary charts. These charts are useful, but are in no way transparent; how are the salaries calculated? What is the sample size and how long was the sample studied? What is the significance of the range? The questions go on. Our findings enabled us to build an alternative salary chart in the accepted format, and our salary calculator will allow you to view the range within which 50% of the sample respondents earn (or where the model predicts 50% of the respondents will be); not just the average forecast. You can try our calculator out here.
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