---
id: "lib-2026-11-07-data-specialists-market-survey-2026-data-2026"
type: "library"
title: "Data specialists market survey 2026 (Исследование рынка data-специалистов, 2026)"
lang: "RU"
org: "DevCrowd with Avito"
source_type: "Survey"
date: "2026-11-07"
week: "2026-W45"
url: "https://nosov.ai/library/lib-2026-11-07-data-specialists-market-survey-2026-data-2026/"
original_url: "https://devcrowd.ru/ds26/"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "DevCrowd's third annual survey of Russian data specialists, run with Avito, gives useful self-reported figures on how AI changed data work, but it is an opt-in survey of 481 people with no sampling or weighting described."
summary_basis: "full-text"
original_minutes: "8"
tags: ["future of work", "enablement", "ai technology", "developer survey", "russia"]
---

# Data specialists market survey 2026 (Исследование рынка data-специалистов, 2026)

Tier B (Useful, limited): DevCrowd's third annual survey of Russian data specialists, run with Avito, gives useful self-reported figures on how AI changed data work, but it is an opt-in survey of 481 people with no sampling or weighting described.

Summary based on the full text.

## In short

DevCrowd's 2026 survey of the Russian data job market, run with Avito, covers 481 data scientists, ML, data and AI/LLM engineers, analysts and MLOps engineers, surveyed 7 September to 6 October 2026. A year ago 3.7% used no AI tools; now only one respondent does, so the survey asked how deeply AI is embedded. The headline: AI took over code production and handed back review. Most people spend more time checking AI output, few have a formal process for it, and most say speed outruns checking.

## Key numbers

- 74.4% / 67.3%: code writers whose coding time fell / whose time checking and fixing AI output rose
- 63%: respondents with no formal process for checking AI-generated output
- 74%: who say output speed now outruns the quality of checking (48% fully agree)
- 58%: using Claude or Claude Code; Claude rose from 22.4% to 36.3% in a year, ChatGPT at 75.2%
- 350,000 roubles: median take-home monthly pay of a senior; junior, mid and senior medians unchanged in a year

## What it reports

- 41% are senior or above; 8.5% chose roles new to the survey (28 AI/LLM engineers, 13 MLOps engineers). 76% of role changes lead toward models.
- Only 49% of data scientists deploy models themselves; 38% of those working with models hold neither a data scientist nor an ML engineer title.
- Tools: Python 76.9%; Greenplum rose from 2.7% to 18%; Claude API in model-building rose from 3.7% to 12.3%, OpenAI API 23.9%, GigaChat and YandexGPT APIs 1.6%.
- AI modes: 46% of juniors use AI only in chat; on average 34.1% mainly use an IDE assistant and 23.6% work with agents.
- Checking: 63% have no formal review of AI output (66% in companies over 10,000 people, 60.3% under 50); 74% say speed outruns checking.
- Trust: 34% trust AI in code but not statistics; 14.2% fully trust it in code, 4.6% in statistics.
- 63% think juniors now find it harder to learn, yet only 24.8% fear their role will be unnecessary in three years.
- 44% learn from LLM answers, a source twice as common as courses. 32% of interviewees met AI bans in job interviews.
- Pay medians flat year on year; 15.5% actively job-hunting.

## How they got it

An online questionnaire by DevCrowd with Avito; respondents self-select. The web report gives percentages and some group splits; it does not describe recruitment, weighting or margins of error.

## Limits

- Self-reported and opt-in; the sample likely tilts toward people in DevCrowd's and Avito's networks, and Avito is also named a most-wanted employer.
- Time changes are perceptions, not measured hours.
- Some subgroups are small (28 AI/LLM engineers).

## Why it matters for companies

The figures put a number on a common pattern: AI moves the workload from producing to verifying, and most teams have not set up a verification process. Data leaders can treat review of AI output as a defined step with owners and criteria, especially for statistics, where trust is lowest. The junior-learning concern and the rise of learning from LLM answers argue for deliberate training paths rather than leaving juniors to the chat window.

## Key passages in translation

> Время на написание кода сократилось у 74.4% тех, кто его пишет, а на проверку и доработку сгенерированного выросло у 67.3%. AI забрал производство и вернул приёмку.

Coding time fell for 74.4% of those who write code, while time on checking and reworking generated output rose for 67.3%. AI took production and handed back acceptance.

> У 63% нет формального процесса проверки того, что сгенерировал AI.

63% have no formal process for checking what AI generated.

> 63% считают, что джунам стало сложнее учиться, но за свою роль через три года боятся только 24.8%.

63% think it has become harder for juniors to learn, but only 24.8% fear for their own role in three years.

Original: https://devcrowd.ru/ds26/