Azaz Rasool

Azaz Rasool

الرياض السعودية
٤ آلاف متابع أكثر من 500 زميل

انضم للمتابعة

نبذة عني

o Director - AI & Data Strategy
o Chief Technology Officer, Straitsbridge Analytics…

الخدمات

طلب مقترح

مقالات Azaz

عرض كل المقالات

الإسهامات

انضم الآن لعرض كل الإسهامات

النشاط

انضم الآن لعرض كل النشاط

الخبرة

  • رسم بياني Al Rajhi Bank

    Al Rajhi Bank

    Riyadh, Saudi Arabia

  • -

  • -

    Hyderabad, Telangana, India

  • -

    Riyadh, Riyadh Region, Saudi Arabia

  • -

    Riyadh, KSA

  • -

    Toronto, Canada

  • -

    Bangalore, Electronics City

  • -

  • -

  • -

    Muscat, Oman

  • -

التعليم

  • رسم بياني INSEAD

    INSEAD

    -

  • -

    https://catalog.ggu.edu/preview_program.php?catoid=13&poid=1403&returnto=764

  • -

    Artificial Intelligence, Machine learning, Data science, Deep learning, Computer Vision , Neural Network

  • -

    Master of Computer Application from MITS College, Jiwaji University

  • -

    Jiwaji University Gwalior (MP)

التراخيص والشهادات

انضم الآن لعرض جميع الشهادات

المشروعات

  • Generative AI in Bank

    -

    #1 Generative AI Use case design, platform and tool selection, vendor evaluation and use case implementation

    #2 Generative AI Use case prioritization, Business case and ROI calculation

    #3 Generative AI use cases test and validation

  • Operationalization of P&L for AI & Data Analytics department

    -

    Operationalization of P&L for AI & Data Analytics department.
    AI & Data Service catalogue
    P&L Structure
    Financial model (Costing, Pricing and Payment models)
    Pilots with internal Business Units and Subsidiaries

  • Strategy and Roadmap for AI & Data

    -

    #1 Designed Strategy and Roadmap for AI & Data for 3 years (2021-23)
    #2 Alignment with Business stakeholders on Business impact, ROI, CAPEX and OPEX
    #3 Use case identification, prioritization and detailing
    #4 Business case building
    #5 Strategy execution, Target operating model, KPI definition and monitoring

  • Big data Architecture design and Implementation for a Canadian Bank

    -

    Architecture Design and implementation of Enterprise Data Provisioning Platform on Cloudera Hadoop
    Data Ingestion design patterns for Mainframe COBOL data files, CSV and RDBMS tables to Hadoop
    Data Extraction design patterns for Consumer applications

  • Customer sentiment analysis using chat data (unstructured)

    -

    Build taxonomy and dictionary of keywords and sentiments
    Load chat files in Hadoop HDFS
    Process chat files, extract sensitive words expressing customer sentiments
    Load extracted keywords and bucket then as positive , negative and neutral sentiments , in separate tables/files
    Visualization/reports in Tableau

  • PayPerView Ad placement optimization

    -

    PPV is an important business line for any cable TV provider and PPV ads are placed in view of increasing the sale of PPV.
    These ads are aired in various part and time of the day. Since Ads get limited slots, if not placed rightly then will not result in conversion and hence loosing the revenue.
    There was a need to Predict ad performance, Minimize ad spend, Utilize ad slots efficiently, Maximize PPV purchases and Understand Ad effectiveness by channel
    Factors considered: Actual ad…

    PPV is an important business line for any cable TV provider and PPV ads are placed in view of increasing the sale of PPV.
    These ads are aired in various part and time of the day. Since Ads get limited slots, if not placed rightly then will not result in conversion and hence loosing the revenue.
    There was a need to Predict ad performance, Minimize ad spend, Utilize ad slots efficiently, Maximize PPV purchases and Understand Ad effectiveness by channel
    Factors considered: Actual ad air time, Channel ad aired on, Window break, Break position
    Models used: nPath, Logistic Regression, Decision Tree

  • Customer Churn prediction

    -

    * High customer churn (1.8% per month) was a cause of concern for the business which needs to be analyzed and addressed.
    * Health of the set top box (STB) was believed to be an important factor for customer churn.
    * There was a need to predict the churn behavior using STB data and accordingly proactive measures to retain the customers can be taken.
    Models to separate churners and non-churners, Correctly identified 77% of churners and 97% of non-churners
    Making Churn prediction…

    * High customer churn (1.8% per month) was a cause of concern for the business which needs to be analyzed and addressed.
    * Health of the set top box (STB) was believed to be an important factor for customer churn.
    * There was a need to predict the churn behavior using STB data and accordingly proactive measures to retain the customers can be taken.
    Models to separate churners and non-churners, Correctly identified 77% of churners and 97% of non-churners
    Making Churn prediction actionable: Roll service truck, Proactively contact customers at Risk, design response scripts
    Factors considered: Days since last health report, Longevity of account, Services subscribed to, # of reboots, Occurrences of low/mid/high signal strength
    Models used: Logistic Regression, Single Factor analysis, Decision Tree

اللغات

  • Urdu, Arabic

    -

  • English

    إجادة تامة على المستوى المهني

المؤسسات

  • NIST Big Data

    No

    ⁩ - الحالي

التوصيات المستلمة

16شخصا قدموا توصية لـAzaz

انضم الآن لعرض

المزيد من أنشطة Azaz

عرض ملف Azaz الشخصي الكامل

  • مشاهدة الأشخاص المشتركين الذين تعرفهم
  • تقديم تعارف
  • تواصل مع Azaz مباشرة
انضم لعرض الملف الشخصي الكامل

ملفات شخصية أخرى مشابهة

استكشاف مزيد من المنشورات

أعضاء آخرون يحملون اسم ⁦⁩Azaz Rasool

4 أخرى باسمAzaz Rasool على LinkedIn

استعرض الأعضاء الآخرين الذين يحملون اسم ⁦⁩Azaz Rasool

اكتسب مهارات جديدة من خلال هذه المواد الدراسية

عرض كل الدورات