◆ Statistics

What a data labeling lead
really does.

23 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.

23evidenced tasks
262,440in the US (2025)
$120,230median pay / year
9systems it runs on
See the 23 tasks ↓Open in the atlasThe full career map →
This is what one task looks like here
Manage large amounts of data
Prepare the labeling backlog for the next quarter: prioritise high-imp…3 sources agree

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The work, task by task

23 tasks
Hands on the work19
Manage large amounts of data+
Prepare the labeling backlog for the next quarter: prioritise high-impact datasets, estimate human-hours per dataset, assign teams for image, text and RDF tasks, and publish the schedule to the project channel by Thursday so hiring can follow.
escoonetwiki3 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Present findings through reports and presentations+
Draft the monthly findings report: summarise label quality trends, model performance by cohort, annotation throughput and blocker incidents, include three visualisations and a one-page executive summary for Tuesday's leadership meeting.
escojdonet3 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Communicate insights to stakeholders+
Prepare stakeholder briefs: create a two-slide summary of risks and recommended actions from the latest label audit, list required decisions from procurement and product by Wednesday, and request a 30-minute review slot with Priya and Marcus.
escojdonet3 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Apply machine learning techniques+
Prototype the ML feature pipeline: select cleansed label sets, engineer candidate features for the recommender, run cross-validation to measure lift, and record feature importance and failure cases for the engineers by end of week.
jdonet2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Merge data sources+
Consolidate the data sources for the master label store: map fields from the CRM export, web logs and legacy RDF dump, deduplicate by customer ID, validate sample joins, then publish the reconciled file for annotation by Monday.
escojd2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Identify business problems and data solutions+
Identify the top three business problems that poor labels are causing in our recommender pipeline, map each problem to a concrete data solution we can staff and budget this quarter, and produce a one-page brief for Priya in product and Mark in engineering by Thursday.
jdonet2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Analyze data to identify patterns and trends+
Run an analysis to surface recurring labeling errors and behavioural patterns across the last three months of annotation work, quantify error rates by labeler team and data slice, and deliver a slide with findings and recommended fixes to send to the annotation managers on Monday.
jdonet2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Categorize and organize data+
Create a clean taxonomy for our image and text labels, collapse redundant tags, propose authoritative parent categories, and publish the updated category list with examples for the annotation team before the next sprint planning on Wednesday.
jdwiki2 agree
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
Watch and assess3
Grow the practice1

What the work runs on

named inside the evidenced tasks
7 tasksAtlassian Confluencepublish the schedule and onboarding notes to the project space
6 tasksApache Sparkexecute scalable training and evaluation jobs over large cleaned partitions for model testing
5 tasksApache Hiveperform large-scale joins, deduplication and validation across heterogeneous datasets for the reconciled table
4 tasksAtlassian JIRAtrack tasks, assignees, estimates and sprint scheduling for large labeling workloads
3 tasksAlteryxcalculate sample sizes, simulate sampling strategies, and produce the documented sampling plan for audits
2 tasksApache Kafkastream labeling and model metric events in near real-time to fuel the dashboard filters and updates
1 taskAmazon Redshifthosts and queries large datasets to determine availability, schema completeness, and data volumes
1 taskApache Hadoopstore and access large public and internal ICT datasets for profiling and sampling
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The same task, four heights

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ExecuteDo today's task, with fewer mistakesyou are here → ImproveMake it easy for the next person to acceptin the atlas → DecideWork out the right move when it is unclearin the atlas → BecomeLearn the pattern so it stops coming backin the atlas →

Can AI actually do this job?

the honest answer

It can

where it genuinely helps
  • Explain the theory behind the work
  • Draft, tidy and structure your writing
  • Rehearse a hard conversation before you have it
  • Build a study plan that fits your gaps

It cannot

where it stops, completely
  • Be in the room where a data labeling lead actually works
  • Carry the responsibility when the call is wrong — that weight stays yours
  • Notice what no one wrote down: the hesitation, the thing left unsaid
  • Live with the outcome

What the work pays

two countries, two different measures

United States

this exact occupation · BLS 2025
  • $120,230 a year — the middle: half earn more, half earn less
  • The lowest tenth earn near $67,240; the top tenth near $199,130
  • 262,440 people employed in this occupation

India

the occupation GROUP, not this job · PLFS via ILOSTAT 2025
  • ₹38,298 a month — the median for Professionals, the group this work sits in
  • India publishes pay by broad occupation group, so this covers many jobs besides this one. It is a shape, not a salary.
read this carefullyThese two numbers are not comparable and must not be converted into each other. One is a yearly figure for this job alone; the other is a monthly figure for a whole family of jobs. What travels between them is the pattern, not the amount: experience lifts pay almost everywhere.

Where the evidence lives

open any of it yourself
O*NETThe US Department of Labor's own record of this job9 of these tasks ESCOThe European skills and occupations register9 of these tasks Job descriptionsReal postings, in the wild14 of these tasks WikipediaHow the world describes this work5 of these tasks US Bureau of Labor StatisticsPay and employment, by occupation2025 figures UK NOS COGPOLY24Assess data and produce reportsnational standard UK NOS TECDT80851Design data processing systemsnational standard

Close to this work

12 nearby
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Questions people actually ask

You’ll split time between hands-on labeling rules and coordinating people. Mornings often start with a stand-up in Atlassian JIRA to assign labeling batches and report blockers.

Afternoons go to quality checks in Amazon Redshift or Hive, reviewing label consistency, and updating instructions in Confluence. Expect meetings with data engineers about Kafka or Airflow pipelines that feed labeled data to models.

You’ll see Jira and Confluence daily for task tracking and documentation. Data work usually touches Redshift or Hive for stored datasets, and Apache Spark or Hadoop for bulk processing.

If labels feed live systems, you’ll check Kafka streams and Airflow jobs. Tools like Alteryx are common for sampling, cleaning, and quick ETL (extract-transform-load) tasks.

Use models for suggestions, not final answers. Run model-assisted labeling where Spark or a model flags likely classes, then have human reviewers confirm—track disagreements in Jira.

Monitor model drift by sampling recent labels and running performance checks in Redshift or Hive; log issues and retrain with corrected labels. Keep label guidelines in Confluence so humans stay consistent.

The Bureau of Labor Statistics lists the occupation group with 262,440 employed, a median salary of $120,230/yr, lowest tenth $67,240, and top tenth $199,130. That gives a typical market range to expect.

Actual pay depends on your region, experience with Spark/Hive/Redshift, and team size. Cite: Bureau of Labor Statistics.

Learn SQL and one data engine: practice queries in Amazon Redshift or Apache Hive. Get comfortable with basic Spark jobs for batch processing and with Airflow for simple pipelines.

Also practice writing clear label guides in Confluence, using Jira for task workflows, and simple ETL in Alteryx or Python. Build a portfolio showing labeled datasets and short dashboards.

A Data Labeling Lead focuses on data quality and human labeling workflows: designing surveys, ensuring consistency, and managing labelers. Your work feeds models rather than building complex models yourself.

Data Scientists build and evaluate models; Data Engineers build data pipelines at scale. You’ll collaborate with both—using Airflow, Kafka, and Hadoop—and focus on annotation, sampling, and guideline enforcement.

Consistent labeling at scale is hardest: writing instructions that different people interpret the same way. Practice by creating short labeling guides, running small pilot batches, and measuring inter-annotator agreement (percentage of exact matches).

Use Jira to track disagreements, update Confluence docs, and repeat. Learn basic statistics to design sampling and agreement metrics, and learn one engine (Spark or Redshift) to check consistency across large datasets.