Noon
Built for Data Scientists

Recruiting data scientists, past the keyword bingo

Source data scientists, ML practitioners, and analytics leaders autonomously - evaluated on real technical depth and problem domains across the whole web, not resume keyword density.
Book a demo →

Built for data hiring

What Noon handles for data teams.
01Distinguishes data science, ML engineering, and analytics
02Technical depth and domain as non-negotiables
03Sources across the whole web, not just LinkedIn
04Personalized outreach that references actual work
05Syncs to your ATS - 20+ integrations

Everyone added "AI" to their resume

The data-talent market is flooded with inflated profiles: every analyst is now a "data scientist" and every data scientist an "ML engineer." Noon evaluates what candidates have actually done - modeling depth, production experience, problem domains, company context - against your specific criteria, so your hiring managers screen real signal instead of keyword noise. Learn more about talent evaluation and tech sourcing channels.

Data hiring pain points Noon removes

Where data-science searches stall.
01Title inflation drowns real signal
02Keyword search cannot judge modeling depth
03Strong candidates field constant recruiter spam
04Analytics vs. ML vs. research profiles get conflated
05Screening technical claims eats data-leader time

Depth requirements as hard filters

Set the technical hard lines - production ML experience, specific domains like NLP or forecasting, research versus applied focus - as non-negotiables Noon never relaxes. Data-leader feedback calibrates the rest, and the agent sources continuously across the whole web with outreach personalized to each candidate’s actual technical work. Learn more about AI recruiting and AI candidate screening.

The autonomous workflow

From requisition to scheduled screen.
01Autopilot sources and evaluates against role-specific criteria
02Technical depth and domain enforced as non-negotiables
03Personalized outreach across email and SMS
04AI Coordinator answers questions and books technical screens
05Activity syncs back to your ATS automatically

Data Scientists FAQ

Can Noon distinguish a data scientist from a data analyst?

Yes. Noon evaluates actual work - modeling depth, production deployment, problem domains, tooling - against your criteria rather than accepting title claims, and your feedback calibrates the bar per role.

Can Noon require production ML experience?

Yes. Set production experience, specific domains, or research depth as non-negotiables the AI never relaxes - candidates without them never reach your pipeline.

How does Noon’s outreach stand out to data scientists?

It is individually personalized to each candidate’s actual technical background and explains why your problem space is relevant to their trajectory. Specificity is what earns replies from people who ignore templates.

Does Noon source beyond LinkedIn for data talent?

Yes. Noon’s agents search across the whole web, which matters for research-oriented candidates whose strongest footprint is outside any one network.

Is Noon secure enough for our security review?

Noon is SOC 2 Type II and GDPR compliant, supports SSO and SAML, and offers dedicated support with custom contracts and invoicing for enterprise customers.
See how Noon works for data scientists hiring - or explore the unlimited plan and enterprise options.
Book a demo →