Maryam E.

AI Researcher

San Francisco, California, United States15 yrs 6 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in machine learning and data mining.
Developed personalized recommendation systems at Amazon.
PhD in Computer Science with extensive research experience.

Stackforce AI infers this person is a Machine Learning Expert in E-commerce and B2C industries.

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Skills

Core Skills

Machine LearningData Mining

Other Skills

Recommender SystemsPersonalized RecommendationCognitive ComputingCustomer-Centric ApproachDeep LearningPython (Programming Language)Artificial IntelligenceMatlabAlgorithmsLaTeXStatisticsC++Data AnalysisResearchC

About

I currently specialize in machine learning (ML), data mining (DM) and recommender systems. Particularly how search queries, advertising platforms and recommender systems can actively learn from each interaction using ML and DM methods. I have extensive experience in applying supervised learning methods including regularized regression/classification, unsupervised learning such as topic models and anomaly detection, graphical models and Hidden Markov Models on big data thanks to Hadoop MapReduce and Apache Spark.

Experience

15 yrs 6 mos

Total Experience

3 yrs 1 mo

Average Tenure

7 yrs 8 mos

Current Experience

Netflix

Machine Learning Research Scientist

Oct 2018 – Present · 7 yrs 8 mos · San Francisco Bay Area

Machine LearningData MiningRecommender Systems

Amazon

2 roles

Machine Learning Scientist Amazon Video

May 2016 – Sep 2018 · 2 yrs 4 mos

Design and develop personalized video recommendation engine.

Machine LearningPersonalized Recommendation

Machine Learning Scientist

Jun 2015 – Sep 2018 · 3 yrs 3 mos

In Amazon, we are using machine learning and cognitive computing to make key assortment and assortment-related decisions such as retailer and customer clustering, depth and breadth of ranges, complementarity of long-tail online and localized in-store ranges, space-constrained assortments, and what might be called the new architecture of assortment attributes. Behind all of these innovations, we expect to see a profound shift to a customer-centric approach and away from the mainstay strategy of product-centric assortment planning.

Machine LearningCognitive ComputingCustomer-Centric Approach