© 2019 Dr. Roy Sasson

 

ABOUT ME

Staff Data Scientist at Google (Head of Product Analytics Group at Waze),
Lecturer at TAU (School of Economics) and IDC (Executive Education), 
Former Chief Data Scientist at Outbrain,
PhD, Econometrics - Tel Aviv University

I am a hands-on Data Scientist and Group Lead. I specialize in Economic applications of machine learning. My work combines academic research with industry data-driven products. You may ask yourself - what do theoretical Economic models have to do with the machine learning that Data Scientists practice? Well.. from my experience.. everything.. Economic applications of Machine Learning range from marketplace management and auction optimization in the field of recommendation systems, to efficient transportation and behavioral nudging people to carpool, and up to optimizing the decision-making processes of NBA coaches and CEOs. A sample of my work in these various domains is detailed below.

If you have any queries, feel free to contact me via LinkedIn or via sassonr@tauex.tau.ac.il 

 

SELECTED RESEARCH & PRODUCTS
(PRESENTED IN CONFERENCES + ACADEMIC JOURNALS)

THE ECONOMICS AND DATA SCIENCE OF ELIMINATING TRAFFIC ALTOGETHER

This short lecture outlines the work my Data Science team does at Waze - helping people find great matches for Carpooling on their daily commute. The lecture highlight how our work is influenced by classic Economic models (specifically by William Vickrey's), combined with modern Economic Models (such as Nudging theories) and Machine Leaning modeling.

NUDGING COMMUTERS TO CARPOOL: A LARGE FIELD EXPERIMENT WITH WAZE

Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. We find a strong relationship between the affinity to carpool and the potential time saving through a high-occupancy vehicle (HOV) lane. Specifically, we estimate that mentioning the HOV lane increases the click-through rate and conversion rate by 133-185% and 64-141%, respectively relative to sending a generic message.

Joint with Maxime Cohen, Michael-David Fiszer and Avia Ratzon.

MIND THE DATA CONFERENCE

This lecture lays my manifesto about the current role that Economists have in the industry, and how they should change their practice if they want to keep the Science of Economics in their hands. The most important lesson from this lecture - "Economists should have their skin in the game", meaning - they should build products instead of consulting, and stand behind their failures.

WHICH INCENTIVES GET PEOPLE TO CARPOOL?
(WAZE LATAM SUMMIT, MEXICO CITY 2019)

This lecture outlines the Analytical work that is being done at Waze about Carpool Incentives: Subsidies, Matching Algorithms, Lock-In Supply and many more.

"FOR YOUR EYES ONLY": CONSUMING VS. SHARING CONTENT ON FACEBOOK

The most comprehensive work ever done to compare what people read online vs. what they share on Facebook. The paper analyzes two types of user interactions with online content: (1) private engagement with content, measured by page-views and click-through rate; and (2) social engagement, measured by the number of shares on Facebook as well as share-rate. Based on more than a billion data points across hundreds of publishers worldwide and two time periods, it is shown that the correlation between these signals is generally low. Potential reasons for the low correlation are discussed, and the notion of private-social dissonance is defined. A more in-depth analysis shows that the dissonance between private engagement and social engagement consistently depends on content category. Categories such as Sex, Crime and Celebrities have higher private engagement than social engagement. On the other hand, categories such as Books, Careers and Music have higher social engagement than private engagement. In addition to the offline analysis, a model which utilizes the different signals was trained and deployed on a live recommendation system. The resulting weights ranked the social signal lower than clickthrough rate. The results are relevant for publishers, content marketers, architects of recommendation systems and researchers who wish to use social signals in order to measure and predict user engagement. 

Joint work with Ram Meshulam.

Link to the full academic paper.

INTRODUCING OUTBRAIN LOOKALIKE AUDIENCES

This is a product that my team at Outbrain developed - a marketer (for example - an online retailer) delivers Outbrain a list of valuable users, for example - users who have made a purchase, not necessarily through Outbrain. We use machine learning models, such as logistic regression, decision trees and matrix factorization to characterise these valuable users' content interests. Such interests (we call those 'features'. There are thousands of those) may include the main content categories they read and not likely to read, publishers they visit and not likely to visit, personas and companies they're interested in etc. Using these models, we identify in real time a user which is not included in the marketer's list, but similar to those users, and recommend them with campaigns by that marketer. 

Research led by Moran Gavish.

USER ENGAGEMENT - BEYOND CLICKS

Outbrain serves over 150 billion content recommendations to more than 500 million users every month. Masses of data tell us what’s driving the mindset of the crowed at each point in time. But how do you analyze if the individual user finds real value in recommendations? And why being satisfied with click-focused-metrics is dangerous for long term growth?
This lecture outlines a Data Scientist’s experience and challenges when analyzing post-click-engagement, in the context of content discovery. This lecture shows examples of how relying on click-focused-metrics might be misleading you in the long run. We will share data of how crowed preferences of consuming content differ from individual user preferences. Finally, we suggest a 3-layer framework for Data Scientists to measure and analyze post-click-engagement, while considering the perspectives of host publishers, marketers and recommendation providers.

TERMINATION RISK AND AGENCY PROBLEMS: EVIDENCE FROM THE NBA

When organizational structures and contractual arrangements face agents with a significant risk of termination in the short term, such agents may under-invest in projects whose results would be realized only in the long term. We use NBA data to study how risk of termination in the short term affects the decision of coaches. Because letting a rookie play produces long-term benefits on which coaches with a shorter investment horizon might place lower weight, we hypothesize that higher termination risk might lead to lower rookie participation. Consistent with this hypothesis, we find that, during the period of the NBA’s 1999 collective bargaining agreement (CBA) and controlling for the characteristics of rookies and their teams, higher termination risk was associated with lower rookie participation and that this association was driven by important games. We also find that the association does not exist for second-year players and that the identified association disappeared when the 2005 CBA gave team owners stronger incentives to monitor the performance of rookies and preclude their underuse. 

Joint with Alma Cohen (Harvard & TAU) and Nadav Levy (IDC).

 

ACADEMIC COURSES

 

INTRODUCTION TO ECONOMETRICS

Tel Aviv University,
The Eitan Berglas School of Economics,
Undergraduate program

DATA-DRIVEN GROWTH

IDC Hertzliya,

Executive Education Program

BIG DATA FOR ECONOMISTS

IDC Hertzliya, 
Arison School of Business Administration
Graduate program
(Also taught at TAU School of Economics)

PROMOTING DATA SCIENCE EDUCATION

 

HEBREW UNIVERSITY'S PPE CONFERENCE

LEARN DATA SCIENCE ONLINE FOR FREE

Even if you don't have the capability of going to college - you can still become a proficient data scientist, almost for free. This is my "greatest hits" list of online classes. It comprises a pretty full survival kit for to-be-data-scientists.

CLICK PREDICTION CONTEST ON KAGGLE

See full contest details here.

Our “Outbrain Challenge” was a call out to the research community to analyze our data and model user reading patterns, in order to predict individuals’ future content choices. The best models were rewarded with cash prizes totaling $25,000. The sheer size of the data we’ve released (100 GBs) was unprecedented on Kaggle, the competition’s platform, and was considered extraordinary for such competitions in general. Crunching all of the data may be challenging to some participants—though Outbrain does it on a daily basis.

Joint work with Ronny Lempel and Ran Locar.

THE TECHNION'S NEW DATA SCIENCE PROGRAM - A REVIEW

BIG DATA ON THE BAR

A light lecture for potential undergraduate students at Dizzy Frishdon, Tel Aviv)