Search

Scott J Sereday

from Rochelle Park, NJ
Age ~41
Get Report
Also known as:
Scott S Sereday
Phone and address:
50 Peek St, Rochelle Park, NJ 07662
Related to:
Larry Brown, 69Josh Sereday, 40Tracy Holster, 40Joshua J Sereday, 38Valerie R Sereday, 39John E Sereday, 65Susan E Sereday, 65Nicholas J Sereday, 36Tracy Holsler, 40Genevieve Holster, 67
Connected to:
Alexey S Serednyakov, 50Nazar R Serednytsky, 52Alexey V Seredov, 40Suzanne L Sereduck, 36Christian J Sereduke, 62Charles H Sereebutra, 53Mikhail Seregine, 43Keri L Sereika, 44Maria M Sereikis, 90Konstantinos Sereleas, 43

Scott Sereday Phones & Addresses

  • 50 Peek St, Rochelle Park, NJ 07662
  • East Rutherford, NJ
  • Clifton, NJ
  • 75 Guilden St, New Brunswick, NJ 08901
  • 31931 Rpo Way, New Brunswick, NJ 08901
  • Franklinville, NJ

Work

Company: Brooklyn nets - East Rutherford, NJ 2011 Position: Statistical analyst

Education

School / High School: COLUMBIA UNIVERSITY- New York, NY 2012 Specialities: Master of Arts in Statistics

Skills

Perl • R • Revolution R • Access • Excel • VBA • SQL • Python • Selenium • SAS • MATLAB • Html

Resumes

Resumes

Scott Sereday Photo 1

Data Scientist, Media Analytics Custom Solutions

View page
Location:
50 Peek St, Rochelle Park, NJ 07662
Industry:
Market Research
Work:
Brooklyn Nets - East Rutherford, NJ since Jan 2012
Statistical Analyst
Buck Consultants Jun 2005 - Feb 2010
Senior Associate, A.S.A., M.A.A.A.
Acme Markets Jul 2001 - Jan 2004
Associate
Education:
Western Oregon University 2009 - 2009
Rutgers, The State University of New Jersey-New Brunswick 2003 - 2005
B.A., Statistics Gloucester County College 2001 - 2003
A.S., Liberal Arts Columbia University in the City of New York 2010
M.A., Statistics
Skills:
Quantitative Analytics
Data Analysis
Strategy
Microsoft Excel
Actuarial Science
Access
Statistics
Financial Modeling
Perl
Python
R
Sas Programming
Visual Basic
Mysql
Machine Learning
Predictive Modeling
Natural Language Processing
Cluster Analysis
Interests:
Researching Tough Questions
Comparative Analysis
Basketball
Sports Statistics
Learning Problem Solving Techniques
Certifications:
The Data Scientist’s Toolbox
R Programming
Practical Machine Learning
Associate, Society of Actuaries
Member, American Academy of Actuaries
License Sl7Yvmny8T
License Sm3Zbahlwj
License Vbeumgkmcv
Coursera Verified Certificates, License Sl7Yvmny8T
Coursera Verified Certificates, License Sm3Zbahlwj
Coursera Verified Certificates, License Vbeumgkmcv
Society of Actuaries
American Academy of Actuaries
Scott Sereday Photo 2

Statistical Analyst

View page
Location:
Greater New York City Area
Industry:
Sports
Work:
Buck Consultants Jun 2005 - Feb 2010
Senior Associate Actuary, A.S.A., M.A.A.A.
Education:
Rutgers, The State University of New Jersey-New Brunswick 2003 - 2005
B.A., Statistics Gloucester County College 2001 - 2003
A.S., Liberal Arts Western Oregon University
Scott Sereday Photo 3

Scott Sereday Rochelle Park, NJ

View page
Work:
BROOKLYN NETS
East Rutherford, NJ
2011 to 2014
Statistical Analyst
Columbia Men's Basketball

2010 to 2011
Statistician
ESPN TrueHoop

2010 to 2011
Writer
BUCK CONSULTANTS, LLC
Secaucus, NJ
2005 to 2010
Senior Associate
Seafood

2001 to 2004
Meat room Department Associate
Education:
COLUMBIA UNIVERSITY
New York, NY
2012
Master of Arts in Statistics
RUTGERS UNIVERSITY
New Brunswick, NJ
2005
Bachelor of Arts in Statistics
GLOUCESTER COUNTY COLLEGE
Sewell, NJ
2003
Liberal Arts
Skills:
Perl, R, Revolution R, Access, Excel, VBA, SQL, Python, Selenium, SAS, MATLAB, Html

Publications

Us Patents

Clustering Television Programs Based On Viewing Behavior

View page
US Patent:
20210329336, Oct 21, 2021
Filed:
Jun 28, 2021
Appl. No.:
17/360822
Inventors:
- New York NY, US
Scott John Sereday - Rochelle Park NJ, US
Xiaoting Liang - Hoboken NJ, US
International Classification:
H04N 21/442
H04N 21/258
G06Q 30/00
Abstract:
Example apparatus disclosed herein are to compare (i) ratios of program ratings to corresponding network ratings with (ii) a threshold to determine adjusted viewing data for respective sites during a monitoring interval, the program ratings and the corresponding network ratings determined for programs tuned on corresponding networks at the respective sites during the monitoring interval, the adjusted viewing data for a combination of a first program and a first site to represent an adjusted amount of time the first program was presented at the first site. Disclosed example apparatus are also to cluster the programs into program clusters based on distances between respective combinations of pairs of the programs, the distances based on the adjusted viewing data. Disclosed example apparatus are further to output information to identify the program clusters.

Estimating Volume Of Switching Among Television Programs For An Audience Measurement Panel

View page
US Patent:
20200014478, Jan 9, 2020
Filed:
Sep 16, 2019
Appl. No.:
16/572135
Inventors:
- New York NY, US
XiaoTing Liang - Hoboken NJ, US
Scott John Sereday - Rochelle Park NJ, US
International Classification:
H04H 60/31
H04H 60/63
H04N 21/658
H04N 21/258
H04H 60/66
H04N 21/438
H04H 60/44
Abstract:
Disclosed example apparatus to determine volume of switching (VoS) among television programs examine first viewing data associated with a first time period and second viewing data associated with a second time period to identify a first set of panelists represented in both the first and second viewing data; in response to a size of the first set of panelists satisfying both first and second thresholds, estimate the VoS based on a first subset of the first viewing data and a second subset of the second viewing data associated with the first set of panelists; and in response to the size of the first set of panelists satisfying the first but not the second threshold, estimate the VoS based on the first and second subsets, and a third subset of the first viewing data and a fourth subset of the second viewing data associated with a second set of panelists.

Clustering Television Programs Based On Viewing Behavior

View page
US Patent:
20190387274, Dec 19, 2019
Filed:
Aug 29, 2019
Appl. No.:
16/555871
Inventors:
- New York NY, US
Scott John Sereday - Rochelle Park NJ, US
Xiaoting Liang - Hoboken NJ, US
International Classification:
H04N 21/442
H04N 21/258
G06Q 30/00
Abstract:
Example program clustering systems disclosed herein are to compare (i) ratios of program ratings to corresponding network ratings with (ii) a threshold to determine adjusted viewing data for respective sites during a monitoring interval, the program ratings and the corresponding network ratings determined for programs tuned on corresponding networks at the respective sites during the monitoring interval, the adjusted viewing data for a combination of a first program and a first site to represent an adjusted amount of time the first program was presented at the first site. Disclosed example program clustering systems are also to cluster the programs into program clusters based on distances between respective combinations of pairs of the programs, the distances based on the adjusted viewing data. Disclosed example program clustering systems are further to output information to identify the program clusters.

Methods And Apparatus To Project Ratings For Future Broadcasts Of Media

View page
US Patent:
20190012684, Jan 10, 2019
Filed:
Jul 16, 2018
Appl. No.:
16/036614
Inventors:
- New York NY, US
Peter Campbell Doe - Ridgewood NJ, US
Scott Sereday - Rochelle Park NJ, US
International Classification:
G06Q 30/02
H04N 21/658
H04N 21/442
H04N 21/258
H04N 21/25
H04N 21/45
G06Q 50/00
H04N 21/466
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification. Disclosed example methods also include building, with the processor, a projection model based on a first subset of the normalized audience measurement data, the first subset of the normalized audience measurement data associated with a first time frame relative to the future quarter, the first subset of the normalized audience measurement data based on the media asset classification, and applying, with the processor, the programming information to the projection model to project ratings for the media asset.

Estimating Volume Of Switching Among Television Programs For An Audience Measurement Panel

View page
US Patent:
20180167153, Jun 14, 2018
Filed:
Oct 31, 2017
Appl. No.:
15/799660
Inventors:
- New York NY, US
XiaoTing Liang - Hoboken NJ, US
Scott John Sereday - Rochelle Park NJ, US
International Classification:
H04H 60/31
H04H 60/66
H04H 60/63
H04N 21/658
H04N 21/258
Abstract:
Examples disclosed herein to estimate volume of switching (VoS) among television programs include determining, based on panelist program viewing data, a first VoS value representing a portion of a decreased amount of tuning by matched panelists to a first program from first to second measurement periods to attribute to an increased amount of tuning by the matched panelists to a second program from the first to second measurement periods; estimating, based on the program viewing data and first VoS value, a second VoS value representing a portion of a decreased amount of tuning by unmatched panelists to the first program from the first to second measurement periods to attribute to an increased amount of tuning by the unmatched panelists to the second program from the first to second measurement periods; and combining the first and second VoS values to determine a third VoS value for a combination of the matched and unmatched panelists.

Clustering Television Programs Based On Viewing Behavior

View page
US Patent:
20180146250, May 24, 2018
Filed:
Oct 31, 2017
Appl. No.:
15/799636
Inventors:
- New York NY, US
Scott John Sereday - Rochelle Park NJ, US
XiaoTing Liang - Hoboken NJ, US
International Classification:
H04N 21/442
H04N 21/258
Abstract:
Example program clustering methods disclosed herein include accessing person-level program viewing data representing lengths of time respective people in an audience have tuned to respective television programs to be clustered. Disclosed example methods also include determining adjusted person-level program viewing data for respective ones of the people having tuned to respective ones of the television programs. For example, first person-level program viewing data for a first person having tuned to a first program is adjusted based on a ratio characterizing a relationship between a first program rating associated with the first person having tuned to the first program and a first network rating associated with the first person having tuned to a first network associated with the first program. Disclosed example methods further include clustering the television programs into clusters based on distances between pairs of the television programs, the distances based on the adjusted person-level program viewing data.

Methods And Apparatus To Project Ratings For Future Broadcasts Of Media

View page
US Patent:
20160150280, May 26, 2016
Filed:
Nov 24, 2015
Appl. No.:
14/951465
Inventors:
- New York NY, US
Peter Campbell Doe - Ridgewood NJ, US
Scott Sereday - Rochelle Park NJ, US
International Classification:
H04N 21/466
H04N 21/45
H04N 21/25
Abstract:
Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification. Disclosed example methods also include building, with the processor, a projection model based on a first subset of the normalized audience measurement data, the first subset of the normalized audience measurement data associated with a first time frame relative to the future quarter, the first subset of the normalized audience measurement data based on the media asset classification, and applying, with the processor, the programming information to the projection model to project ratings for the media asset.
Control profile
Scott J Sereday from Rochelle Park, NJ, age ~41 Get Report