Piperic
similar sites
‹ ProfileAI ReportTools

Sites similar to sidvalecha.com

Sid Valecha - Data Scientist · ranked by shared content topics & relevance
72match
craigdroke.com
Craig Droke | Data Scientist
2 shared topicstechnology-and-computing
72match
fullspectrumdatascientist.com
Full Spectrum Data Scientist
2 shared topicsartificial-intelligence
72match
amankushwaha.dev
Aman Kushwaha ~ Data Scientist
2 shared topicsartificial-intelligence
70match
junclemente.com
Jun Clemente | Applied Data Scientist
2 shared topicsartificial-intelligence
69match
aaryanshah.dev
Aaryan Shah - AI/ML Engineer & Data Scientist
2 shared topicsartificial-intelligence
69match
simplysuvi.com
Suvrat Jain | Data Scientist & AI/ML Engineer
2 shared topicsartificial-intelligence
69match
paluvadisurya.com
Paluvadi Surya | Senior Product Data Scientist
2 shared topicstechnology-and-computing
69match
jsdatascience.com
JS Data Science Services
2 shared topicsartificial-intelligence
69match
abbasataie.com
Abbas Ataie | Software Engineer & Data Scientist
2 shared topicsartificial-intelligence
69match
reidrhodes.com
Reid Rhodes - Data Science Portfolio
2 shared topicsartificial-intelligence
68match
aaryanwadhwani.dev
Aaryan Wadhwani - Software Engineer & Data Scientist
2 shared topicstechnology-and-computing
68match
abhinavamaddha.com
Abhinava Maddha — Data Scientist & Renaissance Soul
2 shared topicsartificial-intelligence
68match
futureofanalytics.net
Will AI Replace Data Scientists? | Future of Analytics
2 shared topicsartificial-intelligence
68match
thebutlercreative.com
Michael Butler — Data Engineer & Scientist
2 shared topicsartificial-intelligence
67match
cosminsanda.com
Data Engineering ∪ Data Science
2 shared topicstechnology-and-computing
67match
cosimameyer.com
Cosima Meyer – Data Scientist, Speaker & Community Builder
2 shared topicsartificial-intelligence
66match
cradl-aws.com
Conrad Lewis | Applied Scientist
2 shared topicsartificial-intelligence
66match
mtnliontech.com
Mountain Lion Tech - Data Science & Engineering Consulting
2 shared topicstechnology-and-computing

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.