Skillenai

drift detection jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-10-21, drift detection appears in 355 job postings indexed by Skillenai over the past 90 days — Data Scientist has the most postings mentioning drift detection, with demand share down 3.3% vs the prior 4 weeks.

Last updated · 90d ending 2026-10-21

Postings · last 90 days
355
Demand vs prior month
down 3.3% vs the prior 4 weeks
Top role · 9.3% of skill postings
Data Scientist
Top hiring metro
New York City

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Frequently asked questions about drift detection

+Is drift detection in demand in 2026?

Yes. drift detection appears in 355 job postings indexed by Skillenai over the 90 days ending 2026-10-21. Data Scientist accounts for the most postings mentioning drift detection (9.3% of all postings mentioning drift detection).

+What jobs require drift detection?

According to the Skillenai jobs index over the 90 days ending 2026-10-21, among roles with at least 20 postings, the highest shares mentioning drift detection are GRC Engineer (9.5% of that role’s postings mention drift detection), AI/ML Scientist (9.1% of that role’s postings mention drift detection), Applied Data Scientist (8.6% of that role’s postings mention drift detection).

+What skills are commonly paired with drift detection?

Across job postings indexed by Skillenai (90 days ending 2026-10-21), drift detection most often appears alongside Python, CI/CD, SQL, machine learning, observability.

+Where is drift detection most in demand?

As of 2026-10-21, the metro areas posting the most jobs requiring drift detection are New York City, London, San Francisco, Bengaluru, Berlin, according to the Skillenai jobs index.

+How can I keep up with new drift detection content and jobs?

Skillenai indexes news, blog posts, and research papers mentioning drift detection alongside the jobs index. You can subscribe to a daily email digest of new drift detection content from your Skillenai account.

+Which skills come before and after drift detection?

The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.

Weekly indexed postings requiring drift detection — last 90 days

Salary distribution

Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized

Career paths around drift detection

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before drift detection

Before drift detectionpython → drift detection: 3 observed employer moves with this skill pairTableau → drift detection: 3 observed employer moves with this skill pairanomaly detection → drift detection: 3 observed employer moves with this skill pairPower BI → drift detection: 2 observed employer moves with this skill pairAzure Databricks → drift detection: 2 observed employer moves with this skill pairdocker → drift detection: 2 observed employer moves with this skill pairaws sagemaker → drift detection: 2 observed employer moves with this skill pairMLflow → drift detection: 2 observed employer moves with this skill pairdriftdetectionpython: 3 movespython3 movesTableau: 3 movesTableau3 movesanomaly detection: 3 movesanomaly detection3 movesPower BI: 2 movesPower BI2 movesAzure Databricks: 2 movesAzure Databricks2 movesdocker: 2 movesdocker2 movesaws sagemaker: 2 movesaws sagemaker2 movesMLflow: 2 movesMLflow2 moves

Skills after drift detection

After drift detectiondrift detection → sentiment analysis: 2 observed employer moves with this skill pairdrift detection → credit risk scoring: 2 observed employer moves with this skill pairdrift detection → NLP: 1 observed employer moves with this skill pairdrift detection → demographic datasets: 1 observed employer moves with this skill pairdrift detection → JAR Files: 1 observed employer moves with this skill pairdrift detection → OpenTelemetry: 1 observed employer moves with this skill pairdrift detection → fine tuning: 1 observed employer moves with this skill pairdrift detection → ci/cd: 1 observed employer moves with this skill pairdriftdetectionsentiment analysis: 2 movessentiment analysis2 movescredit risk scoring: 2 movescredit riskscoring2 movesNLP: 1 movesNLP1 movesdemographic datasets: 1 movesdemographicdatasets1 movesJAR Files: 1 movesJAR Files1 movesOpenTelemetry: 1 movesOpenTelemetry1 movesfine tuning: 1 movesfine tuning1 movesci/cd: 1 movesci/cd1 moves
How to read this chart · view counts

Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.

The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.

Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.

Observed connections and move counts
ConnectionMoves
Before: python3
Before: Tableau3
Before: anomaly detection3
Before: Power BI2
Before: Azure Databricks2
Before: docker2
Before: aws sagemaker2
Before: MLflow2
After: sentiment analysis2
After: credit risk scoring2
After: NLP1
After: demographic datasets1
After: JAR Files1
After: OpenTelemetry1
After: fine tuning1
After: ci/cd1

Roles most likely to require drift detection

Among roles with at least 20 postings in the same period.

RolePostings mentioning skill% of role postings mentioning skill
GRC Engineer29.5%
AI/ML Scientist29.1%
Applied Data Scientist38.6%
ML Platform Engineer48.3%
AI Ops Engineer27.7%
Analytics Engineering Manager27.4%
Software Development Test Engineer26.9%
Product Software Engineer25.4%
Full-Stack AI Engineer14.5%
Infrastructure Automation Engineer14.3%

Roles with the most drift detection postings

RolePostings mentioning skillShare of skill postings
Data Scientist339.3%
Software Engineer298.2%
Machine Learning Engineer267.3%
Backend Engineer164.5%
AI/ML Engineer133.7%
AI Engineer123.4%
ML Engineer102.8%
Product Manager92.5%
DevOps Engineer61.7%
Platform Engineer61.7%

Top companies posting jobs requiring drift detection

Employers ranked by indexed job postings in the last 90 days.

Top companies posting jobs requiring drift detection
CompanyPostings · 90 days
Grafana Labs15
Spacelift14
Wise12
ServiceNow11
Mastercard8
CLERA8
Hopper4
Trustly4
5404
Disney3

Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.

Top metros hiring for drift detection

NamePostingsShare
New York City205.6%
London133.7%
San Francisco133.7%
Bengaluru72.0%
Berlin51.4%
Dublin51.4%
Pune51.4%
Seattle51.4%
Arlington41.1%

Skills commonly paired with drift detection

Python (165)CI/CD (111)SQL (91)machine learning (77)observability (76)model monitoring (68)monitoring (63)AWS (59)MLOps (57)Kubernetes (50)

Explore related pages

How this was computed

Counts derive from the Skillenai jobs index over the 90 days ending 2026-10-21. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning drift detection by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s drift detection postings by all drift detection postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.2% to 0.2%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,610 identified employers and covers 72% of earlier and 73% of latest indexed postings. Windows: 2026-06-23 to 2026-09-21 and 2026-07-21 to 2026-10-19 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.

source
Skillenai jobs index, deduplicated daily
entity_id
4c6146b053b4f7a2
data_as_of
2026-10-21
window_days
90
Compiled by Jared Rand · Data sourced from the Skillenai labor market index