Director, Data Science and Engineering (previously CDO at Identified) @ Workday
Machine Learning, Statistics @
Entrepreneur with a strong background in data science, engineering, product development, sales and marketing. Has built up teams from scratch and been a product visionary. Has been a pioneer in data science with deep expertise in R&D of large-scale, machine learning and statistical modeling systems on some of the largest datasets in diverse domains including search, personalization,
Entrepreneur with a strong background in data science, engineering, product development, sales and marketing. Has built up teams from scratch and been a product visionary. Has been a pioneer in data science with deep expertise in R&D of large-scale, machine learning and statistical modeling systems on some of the largest datasets in diverse domains including search, personalization, recommendation, online advertising and social networks. Frequently invited to speak at a variety of academic and commercial conferences to discuss topics on the boundary of big data, data science and scalable learning algorithms.
Specialties: Entrepreneurship, Data Science, Product Development, Machine Learning, Big Data Engineering, Computational advertising, Search, Recommendation, Social Networks, Experimental Design, Sales, Marketing.
CEO and Co-Founder @ Insnap is a Big Data AI company whose mission is to transform the mobile experience with hyper-personalization. We are hiring data engineers, data scientists, iOS and Android developers. Ping me if you want to work with us. From 2015 to Present (less than a year) San Jose, CAData Science Advisor @ FAMA is a cloud-based, SaaS platform that offers employers actionable insight into a candidate's social media background and digital footprint using Artificial Intelligence and Machine Learning. From 2015 to Present (less than a year) CDO @ Leads/Manages Identified's data science and engineering team which focuses on applying large scale data exploration, data mining and statistical analysis to completely revolutionize the recruitment process.
Drive research and implementation of big data infrastructure and machine learning algorithms to solve problems like entity normalization, disambiguation, ranking and personalization to optimize recruiter ROI, engagement and growth. From 2013 to 2015 (2 years) San Francisco Bay AreaHead of Data Science/Chief Scientist @ Leads/Manages data science team at Workday (Workday acquired Identified) to apply large scale data exploration, data mining and ML algorithms to come up with data-driven insights and products to optimize customer experience. My team works closely with engineering teams to come up with research, prototyping and large scale algorithmic implementation in areas such as big data analytics, recommendations and search among many others. From 2013 to 2015 (2 years) pleasanton, caManager, Data Science & Analytics @ Strategic thought leader and manager of a talented data science and analytics team applying large scale data mining and statistical analysis at Facebook to solve challenging business problems and to measure, understand and optimize user experience in Identity.
I built the group from scratch focused on Timeline, Privacy & Tagging and Profile Completeness to come up with metrics, algorithms and experimental designs for improving the user experience.
I successfully evangelized the importance of accurate instrumentation, statistical thinking and sound experimental design and evaluation, and helping forge a strong cross-functional effort to improve the product experience between engineering, data science, analytics, business intelligence and product management. The core metrics and carefully considered experimental setup and evaluation was key to the successful execution of the product vision and goals. From July 2012 to September 2013 (1 year 3 months) Menlo ParkSenior Data Scientist @ Lead the research, prototyping and implementation of large-scale machine learning algorithms at Netflix to improve recommendations and search ranking.
Researched and developed numerous statistical modeling algorithms that involved learning from signals including searches, plays, impressions and ratings that users gave to content, with the objective function being to maximize the probability of the user playing the recommended title to completion. Patent filed.
Lead the research and implementation of algorithms to understand content demand and trends by using a topic model combined with user searches. This allowed the content buyers to understand trends and use that as a signal for predicting a particular title on Netflix.
Lead the research and development of customer retention modeling to predict how likely is a customer likely to retain (or churn) based on signals extracted from the behavior on the site. From 2011 to 2012 (1 year) Los GatosPrincipal Research Engineer @ Lead the research, prototyping and implementation of statistical learning algorithms for ad monetization and user targeting leveraging data assets like MySpace, Wall Street Journal, IGN, Rotten Tomatoes, PhotoBucket and other News-corp and third party properties.
Envisioned, prototyped and led a team of backend and front end engineers to prototype and develop a keyword marketplace for display advertising called PreSearch. We showcased the prototype in SMX East in NYC in Oct, 2010.
Lead the engineering team in research to come up with an approach categorize users (e.g. MySpace users) into 2000+ commercial custom interest segments (e.g. finance, auto). Implemented multiple models that included supervised methods (SVM) and unsupervised methods (SVD) to learn from structured (e.g. age) and unstructured features (e.g. profile information) to maximize the probability of the user clicking on an ad in the custom category.
Researched and implemented multiple statistical models to predict demographics of user based on features extracted from their online behavior including ad clicks, views, pages and other unstructured and structured features. This formed part of the core model of Fox Audience Network by using MySpace data to learn and allowed us to predict demographics for non-MySpace users on the ad network. From 2008 to 2011 (3 years) Greater Los Angeles AreaSenior Research Scientist / Engineer @ Lead the research, prototyping and development of statistical/data mining algorithms to optimize user experience and revenue on Yahoo properties.
Lead a team of research engineers to come up with an overhaul of the behavioral targeting algorithm and implement a new algorithm that would increase the CTR on display ads by as much as 3X on an average.
Lead a team of engineers to prototype and implement efficient risk detection algorithms to minimize manual/editorial workload by allowing low-risk listings to online immediately. The core algorithm scores the crawled advertiser-submitted landing pages for risk and relevance in editor-specified categories. Designed and implemented efficient resource allocation algorithm for site throttling and in order to not get bottlenecked by slow pages when crawling URLs. Researched and implemented multiple relevance scoring and risk classification algorithms for the advertiser landing pages.
Researched and implemented multiple collaborative filtering algorithms to suggest keywords to advertisers, given the seed terms and/or advertising landing page(s). Investigated and optimized the memory footprint and speed, and implemented fuzzy filters to make the advertiser experience better. Patent granted. From 2004 to 2008 (4 years) San Francisco Bay AreaSoftware Engineer/Consultant @ Involved in design and development of a unified web-based system that handles provisioning, sales and invoicing system for various products. Implemented in Java/J2EE. From January 2004 to June 2004 (6 months) Dallas/Fort Worth AreaGraduate Teaching Assistant @ In-lab assistance, grading, lectures and miscellaneous help to undergrad and grad students in Computer Networks & Architecture, Software Engineering, Introduction to C++. From August 2001 to August 2003 (2 years 1 month) Roanoke, Virginia AreaSoftware Engineer @ Mobile application to automate organizational workflow, and integration of mobile and in-office employee communication. Designed and developed in Java/J2EE.
Mobile application to synchronize and automate work allocation for a big client's field service and repair technicians. Designed and developed in C++/MFC. From June 2000 to June 2001 (1 year 1 month) Noida Area, India
Machine Learning, Statistics @ Stanford University From 2006 to 2007 M.S., Computer Science @ Virginia Polytechnic Institute and State University From 2001 to 2003 Bachelor of Engineering (B.E.), Computer Science & Engineering @ National Institute of Technology Allahabad From 1996 to 2000 Mohammad Sabah is skilled in: Hadoop, Machine Learning, Data Mining, Java, Algorithms, Big Data, Python, Perl, MapReduce, Information Retrieval, Analytics, R, SQL, Hive, Apache Pig, Distributed Systems, Scalability, REST, Statistical Modeling, Java Enterprise Edition, HBase, C++, C, Natural Language..., Software Design, Artificial Intelligence, Software Engineering, Web Applications, Text Mining, Mobile Applications, Matlab, Teradata, Statistics, Computer Science, Data Science, Tomcat, J2EE, Spark, Scala
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