Data & AI hiring

From a vague title
to a tested shortlist.

Your AI recruiting team pins down which data job you mean, pitches the dataset and stack, tests SQL, Python and model reasoning in an editor, and hands you a shortlist with reports.

retention.sql · code editorread & edited · never run
1SELECT cohort_month,2  COUNT(DISTINCT user_id) AS users,3  AVG(d30_active::int) AS d304FROM events e5JOIN users u USING (user_id)6WHERE e.type = 'session'7GROUP BY 18ORDER BY 1;
Join issue found · 2 / 3 edge cases named
SQL round doneWindow functions · strong
Model discussionEvaluation reasoning

Trusted by hiring teams at

WalmartBMWBooking.comRazorpayDocuSignPaytmRandstad DigitalKPI PartnersCodeRabbitDeccan AISpace MatrixPhilogenUnifi CapitalenParadigmphamaxMastercomAnthriqSolinasPopular Motor CorporationPopular Motor VenturesStelloSurf

Data and AI profiles all list the same libraries. EmployLabs finds the job behind the title, reaches the people, tests how they reason and hands you a shortlist.

What data leaders tell us

Titles blur. Skills hide.

One title, five jobs

A “data scientist” may be an analyst, an engineer or a researcher. Résumés rarely say which.

Notebooks look better than they are

A polished project can hide that the candidate never shipped anything to production.

Technical screens need scarce reviewers

The people able to judge a model are the same people building yours.

Who you hire

From pipelines to models.

Data engineering

Pipelines, warehouses and the reliability of both.

Data engineerAnalytics engineerETL developerBig data engineerData platform engineer

Analytics & BI

People who turn tables into decisions.

Data analystBusiness analystBI developerProduct analystInsights managerReporting analyst

ML & AI

Models, evaluation and getting them live.

Machine learning engineerData scientistNLP engineerComputer vision engineerMLOps engineerApplied scientist
The flow for data teams

Find the analyst, test the thinking.

The same three agents, aimed at data and AI profiles.

MeeraAI recruiter
Meet Meera
Find

Separate analysts from engineers

Meera asks which of the five jobs sits behind your title, then ranks profiles on public work: papers, repos, dashboards and tools they actually use, with the reason shown.

YOU NO LONGER
  • guess what a title means
  • filter by library keywords
Separate analysts from engineers
ZiaOur career strategist
Meet Zia
Reach

Outreach that names the data problem

Zia pitches the dataset, the stack and whether the role ships models or reports. She answers pay band and remote policy questions from your job settings.

EmailWhatsAppSMSCallXReddit
YOU NO LONGER
  • rewrite the pitch per profile
  • follow up on every silence
Zia · ML engineerZia · every channel
Zia

Hi Mira, an ML engineer role on evaluation and data quality. Remote within India.

Mira

Saw your note. Which models does the team work on?

Zia

Evaluation and ranking, mostly. Salary band and stack are in the brief.

Zia

Booked: interview Monday 17:00 with a SQL round

NairaAI interviewer
Meet Naira
Interview

SQL, cases and model talk

Candidates read and fix SQL and Python in an editor Naira sees, work a case about a falling metric, and explain how they would evaluate a model they built.

YOU NO LONGER
  • screen every analyst yourself
  • check SQL by hand
Case · Orders fell 12% last weeklive
StepCandidate saidRating
Define metricOrders vs sessionsStrong
SegmentBy city and deviceStrong
HypothesesPricing, outageFair
Next queryFunnel by dayStrong
ELYouThe final call
See a sample report
Decide

Pick who goes to the panel

The report rates each module and quotes the moment behind it. Your lead reads the finalists by link and spends the panel on the people who cleared it.

YOU NO LONGER
  • sit in first-round panels
  • compare notebooks
Naira report cardVoice · Naira

Maya ThompsonApplied for Collections SpecialistInterviewed Sep 27, 2026, 38 min

74.0/ 100 overall

AlignedIntegrity 96%English (CEFR) B2
“Clear, calm caller who secured a payment plan without pressure tactics; audit caught 4 of 5 compliance slips. Speaks fluently at a natural call pace.”

Handle a Customer Who Disputes a Past-Due Balance

M2Voice only
Module Score4.1 / 5

Strengths

  • Verified identity before discussing the balance
  • Offered a concrete 3-month plan unprompted

Weaknesses

  • Slight hesitation stating the exact due date
  • Did not repeat the total balance for confirmation

Evidence (transcript quotes)

  • “I understand this is frustrating — let's find a plan that works for you and confirm it today.”
Inside the flow

Data hiring, start to shortlist.

From the job behind the title to a shortlist your lead trusts.

Brief

Which data job?

Meera asks: analyst, engineer or scientist? Then she ranks.

Data scientist, ranking, remote
Analyst?Engineer?Scientist?SQLPython
Reach

Pitched on the dataset

Zia names the data and stack on email, X, Reddit or WhatsApp.

Zia
SQL

SQL in the editor

Joins and windows read and fixed in an editor Naira sees. Never run.

SELECT region, SUM(amt)FROM salesGROUP BY regionHAVING SUM(amt) > 1e6;Filter issue explained
Case

Metric drops

Reasons from a falling number to a hypothesis and a next query.

Orders240
Sessions180
Checkout64
Paid40
Shortlist

Strengths by module

Per-module scores and quotes your lead reads by link.

SQL90
Python78
Statistics60
Communication82
Budget

Free search, capped spend

Search and scoring cost 0 credits; spending stops at your cap.

Budget900
creditsCap 1,500
Before & after

Data hiring, before and after

The usual way
“Data scientist” could mean anything
Rewriting the pitch for every profile
Course certificates as proof
Case rounds need your best analyst
Finalists compared on gut feel
Screening costs nobody capped
ELWith EmployLabs
Meera pins down which job it is before searching
Zia pitches the dataset and stack, then follows up
SQL and Python read and fixed in the interview
Naira runs the case and writes up the reasoning
A shortlist with per-module strengths and quotes
A budget cap per role; search stays free
By the numbers

The engineering behind every hire.

Sourcing800M+professional profiles to search
Engagement5M+candidate conversations on every channel
Interviews50K+AI interviews completed
Shortlists10K+shortlists delivered
Time saved200K+recruiter hours saved
Coverage40+industries hired for
FAQ

We have the answers

How it works, what it costs, and what your candidates see.

How does EmployLabs help hire data and AI talent?

EmployLabs gives you an AI recruiting team: Meera searches professional profiles and ranks data and AI candidates with the reason behind each score, drawing on public work. Zia engages them on six channels and books interviews. Naira interviews by voice using documents and code viewers, or grades a take-home as Done, Partial or Not done, then follows up by voice.

Can Naira test SQL and Python in the same interview?

Yes. Candidates read and edit SQL and Python in a code editor Naira sees and explain each change by voice. Code is never run.

How are case questions scored?

Naira asks a business case, follows up on assumptions and rates the structure of the reasoning. The report quotes the moment behind each rating.

Can it judge a machine learning candidate?

It can run a model-evaluation discussion: metrics, leakage, monitoring and trade-offs. Questions are built from your role, so put the research depth you need in the brief.

Do you support Spark, dbt or specific tools?

Candidates talk through the tools in your brief by voice, with follow-ups built from your role, and read code in the editor. Meera also ranks people on the tools they actually use.

Can I upload applicants instead of searching?

Yes. Upload résumés and they are scored for free, then invite the ones you want to interview.

How does Zia reach data people?

On email, WhatsApp, SMS, calls, X and Reddit, with a pitch about the dataset and the stack. She answers pay band and remote questions from your job settings and books the interview.

Let's talk
Still have questions? We're here to help.

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already out there.

Tell us who you need. We find, contact and interview them, and you meet the best.

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