Amin Jamalzadeh

Amin Jamalzadeh

台灣 臺北市 台北
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關於

As the Head of Data Science and Analytics at Zalando, Europe's leading online fashion…

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工作經歷

  • Coupang 圖片

    Coupang

    Taiwan

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    Berlin, Germany

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    Cambridge, United Kingdom

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    Cambridge, United Kingdom

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    Cambridge, United Kingdom

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    Hull, United Kingdom

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    Iran, Tehran

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    Statistical Research and Training Center (SRTC)

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教育背景

  • 英國德倫大學 圖片

    University of Durham

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    My PhD research focused on providing statistical methods for web usage data, clickstream data. This study involved different aspects of web analytics, from univariate exploratory analysis to modelling web-page browsing patterns. I also investigated the causal relationship between the general clickstream information and online conversion using logistic regression and tree-based models (CART modelling). I also developed the application of a mixture of hidden Markov models (MixHMM) in Bayesian…

    My PhD research focused on providing statistical methods for web usage data, clickstream data. This study involved different aspects of web analytics, from univariate exploratory analysis to modelling web-page browsing patterns. I also investigated the causal relationship between the general clickstream information and online conversion using logistic regression and tree-based models (CART modelling). I also developed the application of a mixture of hidden Markov models (MixHMM) in Bayesian framework to model web browsing behaviour using sequences of web pages viewed by users of an e-commerce website. The MixHMM provided a classification of web pages based on users’ browsing patterns so that web designer could find out whether this pattern matches the expected web page links (find the full thesis in http://etheses.dur.ac.uk/3366/1/thesis.pdf ). Some results of this study have been published in the Journal of Applied Statistics.

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    Two years of course-based MSc programme in Socioeconomic Statistics. My dissertation entitled “Logistic regression for Case-Control studies with complex survey sampling”. I was reviewing the methodology developed by Scott and Wild (2000) and later by Skinner et al (2000) on using pseudo-likelihood function when fitting a logistic model on Sample survey data. We also reviewed the linearization technique to provide an estimate for variance when assumption of independence identical distribution…

    Two years of course-based MSc programme in Socioeconomic Statistics. My dissertation entitled “Logistic regression for Case-Control studies with complex survey sampling”. I was reviewing the methodology developed by Scott and Wild (2000) and later by Skinner et al (2000) on using pseudo-likelihood function when fitting a logistic model on Sample survey data. We also reviewed the linearization technique to provide an estimate for variance when assumption of independence identical distribution assumption of keywords do not met. We applied this method on a survey data collection on Isfahan Healthy Heart Project using a sampling survey. I introduced the application of pseudo-maximum likelihood method for case-control studies when case, or control (or both) are collected using a sample survey design. The proposed approached also extensively investigated through simulation studies.

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榮譽獎項

  • EConsultancy the Digital 2014

    EConsultancy

    In past seven years I have been developing practical predictive models using computer simulation models and prescriptive optimization algorithms for predicting the performance of and managing digital marketing advertising channels in academics, as well as for industry. Result of this work has won a prestigious award of Digital 2014 for managing PPC account for Argos.

  • Northern Marketing Awards 2014: Summit Scoops Retail Campaign of the Year

    Summit Forecaster - Helping Argos predict and profit from the future

    Summit’s ability to predict future revenues from marketing helping Argos drive the most profitable year yet from digital sales has seen the agency win Retail Campaign of the Year at the Northern Marketing Awards.

    The judges were impressed by Summit’s strategy and proprietary technology that uses predictive analytics to predict when consumers are likely to purchase a product. This has given Argos a distinct competitive advantage by ensuring the right search ad is shown at the right time…

    Summit’s ability to predict future revenues from marketing helping Argos drive the most profitable year yet from digital sales has seen the agency win Retail Campaign of the Year at the Northern Marketing Awards.

    The judges were impressed by Summit’s strategy and proprietary technology that uses predictive analytics to predict when consumers are likely to purchase a product. This has given Argos a distinct competitive advantage by ensuring the right search ad is shown at the right time, on the right device, while maintaining low cost of sale. Data from sources such as weather, location, seasonality, promotions and stock availability are analysed each day to help anticipate customer purchase behaviour resulting in greater accuracy of the predictions each day.

    But it was the incredible commercial results that really helped Summit scoop the winning spot. The ‘ultra optimisation’ has seen Argos achieve new records in sales from PPC all while maintaining cost of sale.

    The level of complexity and automation required to run the Argos campaigns is impressive, with Summit running ‘big data’ through millions of predictive models and running around 2 billion calculations a day. These calculations are then translated into accurate budget recommendations, revenue forecasts as well as bid optimisations for Argos’s hundreds of thousands of paid search campaigns. These recommendations are automatically published to Google within seconds.

    http://www.northernmarketingawards.co.uk/winners-2014

  • Overseas Research Students Awards Scheme (ORSAS)

    Durham University - Department of Mathematical Sciences

    ORSAS was set up to attract high-quality international students to the United Kingdom to undertake research by providing the difference in fees charged to overseas students. Participating institutions received an annual grant from the relevant UK funding body to fund ORSAS. The grant was allocated by formula and set to run from 2006-07 until the end of 2008-09. ORSAS funding was phased out gradually over 2009-10 and 2010-11.

    see http://www.orsas.ac.uk/ for more details

語言能力

  • Persian

    母語

  • English

    中高級（商務會話）

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