Divya Gadde
Data AnalystAI/ML @ Netflix
Netflix
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML @ Netflix
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
The University of Texas at Austin
Master's Degree, Electrical and Computer Engineering
January 1, 2013 – January 1, 2015
IIT Madras
Bachelor's Degree, Electrical Engineering with a minor in Operations Research
N/A – Present
Netflix
Research Engineer
March 1, 2023 – Present
Los Gatos, California, United States · Hybrid
Staff Machine Learning Engineer
November 1, 2021 – March 1, 2023
San Francisco Bay Area
Software Engineer - Machine Learning
November 1, 2016 – November 1, 2021
Sunnyvale, California
@WalmartLabs
Relevance Engineer
August 1, 2015 – November 1, 2016
San Bruno
The University of Texas at Austin
Teaching Assistant for Business Statistics
August 1, 2014 – May 1, 2015
National Instruments
Intern - RF Communications Engineer
May 1, 2014 – August 1, 2014
Austin
iRunway
Associate
January 1, 2011 – June 1, 2013
Bangalore
IBM
Software Development Intern
May 1, 2009 – August 1, 2009
Bangalore, India
Fault Tolerant Library Portal
January 1, 2015 – Present
•Developed a fault tolerant library server-client system that accepts requests from multiple concurrent clients using TCP protocol. Fault tolerance was achieved using replication. •Implemented Lamport’s mutex algorithm to maintain consistency between the replicated servers.
Log Data Analytics with Hadoop
January 1, 2015 – Present
•Assembled individual log entries of an online automotive marketplace into user sessions. Filtered these user sessions into different categories and determined their click statistics. •Created vehicle impression statistics by joining two different data sources using a reduce-side join. The data structures used for events and sessions were created using Apache Avro.
Social Network based Recommender Systems
September 1, 2014 – December 1, 2014
Trust based recommender systems use new approaches that use the social network of users to provide recommendations. Collaborative filtering methods can not make recommendations for ‘cold start users’ that have rated very small number of items. Their coverage is usually poor. Trust based systems use the trust network of cold start users to make recommendations. We evaluated the performance of user based and item based collaborative filtering methods as well as three different trust based recommender systems - Pure Trust, Random TrustWalker, and Augmented Trust - over Epinions dataset. We use RMSE, Coverage and F Measure to compare the performance of these algorithms. Both Random Trustwalker and Augmented Trust methods make use of trust network as well as collaborative filtering to make recommendations, thereby improving the coverage tremendously for a marginal tradeoff in accuracy. For cold start users, we saw that the coverage almost doubles for trust based methods when compared to traditional methods. Finally, we also experimented with an algorithm that extends augmented trust to give top-N recommendations.
Cultural Fit Analysis
The candidate's career trajectory shows a strong focus on machine learning, data science, and analytics within large tech companies, which aligns with a data-driven culture. The personal projects, particularly 'Social Network based Recommender Systems' and 'Log Data Analytics with Hadoop', demonstrate initiative and a deep interest in data-related challenges. However, the target role is 'Data Analyst', which might be a slight mismatch given the candidate's senior-level Machine Learning Engineer experience. While the skills are transferable, the depth of ML engineering might exceed typical Data Analyst requirements, potentially leading to overqualification or a desire for more advanced ML roles.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting strong problem-solving and analytical skills. Experience as a Teaching Assistant implies communication and mentoring abilities. The diverse roles across different companies (Netflix, Twitter, LinkedIn, WalmartLabs) suggest adaptability and a capacity to integrate into various operational environments.