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Igor Perisic Email & Phone Number

VP Engineering; AI, Privacy and Data at LinkedIn
Location: Los Altos, California, United States 16 work roles 2 schools
1 work email found @google.com 11 phones found area 650, 860, 617, 510, and 212 LinkedIn matched
✓ Verified Jul 2026 4 data sources Profile completeness 100%

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Direct phone (650) ***-****
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Current company
Role
VP Engineering; AI, Privacy and Data
Location
Los Altos, California, United States

Who is Igor Perisic? Overview

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Quick answer

Igor Perisic is listed as VP Engineering; AI, Privacy and Data at LinkedIn, based in Los Altos, California, United States. AeroLeads shows a work email signal at google.com, phone signal with area code 650, 860, 617, 510, 212, and a matched LinkedIn profile for Igor Perisic.

Igor Perisic previously worked as VP Engineering at Linkedin and Expert at Oecd.Ai. Igor Perisic holds Ph.D., Statistics from Harvard University.

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{first_initial}{last}@google.com
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Profile bio

About Igor Perisic

Senior Technology leader building AI models and Data Systems that achieve high availability and throughput with very low latencies. I am passionate about Data at scale; the distributed systems required to play with it; the AI methods, models and algorithms to uncover its patterns and the inferences and products one can draw from it. While I strive to be the best mentor I can, I am in awe of those that can truly teach and inspire.

Listed skills include Distributed Systems, Hadoop, Scalability, Information Retrieval, and 34 others.

Current workplace

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LinkedIn
Linkedin
VP Engineering; AI, Privacy and Data
Website
AeroLeads page
16 roles

Igor Perisic work experience

A career timeline built from the work history available for this profile.

Vp Engineering

Current

Sunnyvale, Ca, Us

Aug 2023 - Present

Expert

Current

Paris, Île-De-France, Fr

Contributing to the AI, Data Privacy working group with OECD.AI, focusing on developing policies and guidelines at the intersection of Data Privacy and AI.Collaborated with the AI Risk & Accountability workstream to identify potential risks and establish accountability measures in AI technologies.Previously participated in the AI definition working group, contributing insights and expertise to shape the definition of an AI System (definition which was later picked up by the EU AI Act).

Jun 2020 - Present

Advisor

Current

Zürich, Zürich, Ch

Jul 2023 - Present

Advisor

Current

Mountain View, California, Us

Jul 2023 - Present

Advisor

Current

San Francisco, Ca, Us

Helping to connect the dots between Switzerland and North America in science, education, art, and innovation

Nov 2009 - Present

Vp Engineering And General Manager

Mountain View, Ca, Us

GM of the Ads Privacy and Safety team. Responsible for ensuring that the entire (Google) Ads Ecosystem is a safe and thriving environment compliant with regulations globally. Defining Google's Ads policies and building the processes and AI systems to enable their enforcement at scale. Google Ads lead on the Privacy Sandbox.

Sep 2021 - Apr 2023

Chief Data Officer And Vp Of Engineering

Sunnyvale, Ca, Us

My role as a Chief Data Officer is to closely collaborate with our product, security, and legal teams to ensure that we are implementing the right technology, policies, and controls to rapidly (and safely) scale our portfolio of product to an ever-broadening audience of members and customers. Data has been and will continue to be the lifeblood of LinkedIn. To continue to deliver on the promise of data science, a continuous investment for excellence in data infrastructure and relevance are absolutely necessary for us to deliver on our mission and vision. Moreover, our "Members First" approach requires us to be ever vigilant with respect to data stewardship and protection. As an Engineer, my team builds and maintains our core data infrastructure, creates and deploys AI models to personalize our members' experiences, provides analytics platforms/capabilities and analyses for our business and product portfolio. In addition, my engineering responsibilities include overseeing open source efforts and adoption at LinkedIn.In summary, my work revolves around making sure that we are able to leverage our data in ways that are effective, safe and reflective of our company values.

Feb 2017 - Sep 2021

Vp Engineering

Sunnyvale, Ca, Us

Leading the Data team at LinkedIn, a horizontal team building infrastructure at LinkedIn and creating personalized Member experiences through Machine Learning and AI. Making sure that our infrastructure stays steps ahead of our product demands and the requests of our AI models. Building AI models that strive to create a delightful and personalized experience for our Members. Continuing to drive our ability to make product and business decisions by leveraging our Data.

Jul 2013 - Feb 2017

Sr. Director Of Engineering

Sunnyvale, Ca, Us

Responsible for our Data and Analytics infrastructure and products. Created and grew a team centered around building experiences and products that leverage our Data.- AI: Creating a data mining platform and ML models for personalizing our members experiences. These cover high volume realtime recommendations and ranking as well as ads targeting. Applying various information extraction techniques to standardize core data components that feed our models.- A/B testing environment: Building a decision engine on top of our core A/B infrastructure to enable rapid analysis ofexperiments. Key aspects are 1) ensuring the quality and integrity of our data throughout our data pipeline 2) uniformity of metrics definitions and computations and 3) an ability to 'simply' draw inferences about test performance across all metrics.- Online Data Infrastructure: Scaling our Data Infrastructure to support our product needs. Core components include Kafka our Open Source PubSub Messaging system, Voldemort a distributed KV storage system and our new Espresso NoSQL distributed data storage. - Offline Infrastructure: Building the tools and processes necessary to enable Hadoop to become our source of truth with respect to Data.- Search: From the Member experience to the indexers, creating a multilingual, distributed social search with a social network twist.- Social Graph: Building and deploying a new proprietary core distributed graph engine that scales with our Membership growth and the complexities of new APIs.- Open Source: Continuing to Drive LinkedIn's Open Source efforts and our involvement with the Community.

Nov 2011 - Jul 2013

Director Of Engineering; Search, Network And Analytics

Sunnyvale, Ca, Us

Started from a team of 2 Engineers and grew it to 100+ Engineers mostly focused on High Throughput Distributed System and Machine Learning. During these earlier dates at Linkedin, our major challenge was scaling our systems to support our exponential growth. In this time window, LinkedIn’s membership grew from 14M to about 150M and the number of page views (yearly) grew from ~1B to about ~ 27B. Started and lead 3 engineering tracks at Linkedin while at the same time building the case for Data and Machine Learning at LinkedIn. These tracks were:- Search: LinkedIn is a social network, search needs to blend your network with each request. Hence the standard cream of the crop approach won't work without serious tweaks. We built our system on top of Lucene and architected a distributed system combining real-time search with faceted navigation.- Social Graph: At Linkedin, Cloud is the service that supports all realtime inquires to the social graph. The service is core and critical all that LinkedIn does. The team had the daunting task of scaling the realtime service as well as creating new APIs to access the graph for new product feature.- Machine Learning, AI: The ML and Data Science team builds new data-driven experience for our Members. By 1) Creating and deploying new experiences such as People You May Know, Who Viewed My Profile or Jobs You May Be Interested In; 2) Building an infrastructure that allows us to manipulate Data at scale to create these experiences; 3) Online and Offline Data Mining systems for training and realtime serving recommendations and 4) Designing and building a world class A/B testing environment.In parallel to making sure we were making progress on these tracks, started two external programs to provide visibility about the quality of our work; our internal Open Source Program and top tier industry conference publications, with a focus on publishing only that which was actually deployed to the site

Oct 2007 - Nov 2011

External Advisory Board Uc Santa Barbara Data Science Initiative

Santa Barbara, Ca, Us

A member of the External Advisory Board provides invaluable industry feedback to DSI leadership on curriculum and the continuously evolving academia and industry needs. The relationship between the External Advisory Board members and DSI is a true partnership. It merges the leadership, expertise, and resources of its members with the visionary goals of the UCSB faculty in data science resulting in positive and transformative solutions to some of our world’s most pressing challenges.

Mar 2019 - Aug 2021

Senior Product Manager

Redmond, Washington, Us

Member of Search Labs. Search Labs is one of Microsoft's three labs and is mainly dedicated to Search as well as the Internet. Search Labs is headed by Rakesh Agrawal and migrated to Microsoft Research in 2007 under Harry Shum. Most of the work is really confidential but main projects where:- Creation of Galileo, a data mining platform built upon Cosmos, Microsoft's distributed computing environment.- Core ranking development covering new ranking features, feature pruning as well as all training and validation steps.- Task Based Searches as a new Search Paradigm- Personalization and vertical searches

Aug 2006 - Oct 2007

Statistical Consultant

Redwood City, Ca, Us

Turn is building an automated targeted ad (mainly CPA-based) market. I essentially worked with the Chief Scientist (Dr. James Shanahan) and Dr. Jerome Friedman (Stanford). Delivered:- Metrics to evaluate the performance of predicted probabilities of actions on the live system. - Models to adjust the predicted probabilities of actions to what was observed. The model was later deployed and also used to determine ad rotation schemes.

Apr 2006 - Aug 2006

Cto

Healthline.Com
Jul 2005 - Apr 2006

Visiting Scholar

Stanford, Ca, Us

Visiting Scholar with the department of Sociology. Had fun with Prof. Granovetter and the Silicon Valley Networks Analysis Project (SiVNAP).

Apr 2005 - Mar 2006

Chief Scientist

Balik Pulau, Pulau Pinang, My

Sep 2000 - Jul 2005
Team & coworkers

Colleagues at LinkedIn

Other employees you can reach at dukelong.com. View company contacts →

2 education records

Igor Perisic education

Ph.D., Statistics

Harvard University

Diplome D'Ingenieur, Mathematics

Epfl
FAQ

Frequently asked questions about Igor Perisic

Quick answers generated from the profile data available on this page.

What company does Igor Perisic work for?

Igor Perisic works for LinkedIn.

What is Igor Perisic's role at LinkedIn?

Igor Perisic is listed as VP Engineering; AI, Privacy and Data at LinkedIn.

What is Igor Perisic's email address?

AeroLeads has found 1 work email signal at @google.com for Igor Perisic at LinkedIn.

What is Igor Perisic's phone number?

AeroLeads has found 11 phone signal(s) with area code 650, 860, 617, 510, 212 for Igor Perisic at LinkedIn.

Where is Igor Perisic based?

Igor Perisic is based in Los Altos, California, United States while working with LinkedIn.

What companies has Igor Perisic worked for?

Igor Perisic has worked for Linkedin, Oecd.Ai, Decentriq, Kumo.Ai, and Swissnex San Francisco.

Who are Igor Perisic's colleagues at LinkedIn?

Igor Perisic's colleagues at LinkedIn include Rakesh Kashyap, Alex T., Juliette Faraut, Suyash Gupta, and Alex Petrilli.

How can I contact Igor Perisic?

You can use AeroLeads to view verified contact signals for Igor Perisic at LinkedIn, including work email, phone, and LinkedIn data when available.

What schools did Igor Perisic attend?

Igor Perisic holds Ph.D., Statistics from Harvard University.

What skills is Igor Perisic known for?

Igor Perisic is listed with skills including Distributed Systems, Hadoop, Scalability, Information Retrieval, Data Mining, Recommender Systems, Data Analysis, and Statistics.

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