Shaan Barca Perlambang

Data Analyst at Manajemen Pelaksana Program Kartu Prakerja
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Contact Information
us****@****om
(386) 825-5501
Location
Jakarta, Indonesia, ID
Languages
  • Bahasa Indonesia Native or bilingual proficiency
  • English Native or bilingual proficiency
  • Japanese Limited working proficiency

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Credentials

  • Learn Tableau for Data Visualization Course
    Codecademy
    Mar, 2023
    - Nov, 2024
  • Project Planning: Putting It All Together
    Google
    Feb, 2023
    - Nov, 2024
  • Foundations of Project Management
    Google
    Dec, 2022
    - Nov, 2024
  • Matrix Algebra for Engineers
    The Hong Kong University of Science and Technology
    Dec, 2022
    - Nov, 2024
  • Project Initiation: Starting a Successful Project
    Google
    Dec, 2022
    - Nov, 2024
  • Neural Networks and Deep Learning
    DeepLearning.AI
    Jan, 2021
    - Nov, 2024
  • Machine Learning
    Stanford University
    Nov, 2020
    - Nov, 2024

Experience

  • Prakerja
    • Indonesia
    • Government Relations Services
    • 100 - 200 Employee
    • Data Analyst
      • May 2023 - Present

      PMO of Coordinating Ministry for Economic Affairs Indonesia PMO of Coordinating Ministry for Economic Affairs Indonesia

  • Pemerintah Provinsi DKI Jakarta
    • Indonesia
    • Government Administration
    • 400 - 500 Employee
    • Data Analyst
      • Sep 2022 - Dec 2022

      ● Hired for a project to design database, data warehouse and provide KPIs to track performance of creative business in Jakarta (Disparekraf) ● Prototyped Tableau Dashboard to visualize data ● Educated staff on how to utilize dashboard ● Designed RCT experiments to track effectiveness of government program(s) ● Hired for a project to design database, data warehouse and provide KPIs to track performance of creative business in Jakarta (Disparekraf) ● Prototyped Tableau Dashboard to visualize data ● Educated staff on how to utilize dashboard ● Designed RCT experiments to track effectiveness of government program(s)

  • Tapestry
    • United States
    • Retail Apparel and Fashion
    • 700 & Above Employee
    • Data Scientist
      • Apr 2022 - May 2022

      • Querying data from Amazon Redshift DB • Creating Time Series forecast for its subsidiary companies using forecasting methods such as SARIMA(X) and GradientBoosting methods. • Identified “levers” to the sales and marketing team to use to increase sales in retail stores. • Querying data from Amazon Redshift DB • Creating Time Series forecast for its subsidiary companies using forecasting methods such as SARIMA(X) and GradientBoosting methods. • Identified “levers” to the sales and marketing team to use to increase sales in retail stores.

  • Samudera Indonesia
    • Indonesia
    • Transportation, Logistics, Supply Chain and Storage
    • 200 - 300 Employee
    • Data Scientist
      • Apr 2021 - Nov 2021

      ● Samudera Indonesia group is one of the largest maritime companies in Indonesia. ● Main objectives were to redesign database schema, conduct time series forecasting to optimize port operations ● Actions taken were visiting ports to understand the business process of ports, what data was collected, how it was collected and its purpose ● Using R and Python, SARIMA and Prophet models were built to get time series decomposition and forecast and to better understand where and when cargo volume would change and hypothesize why it occurred. ● The forecast produced a MAPE score < 0.2. Results of analysis were then presented to Head of Research and CFO of Samudera Port Show less

  • EIDARA MATADATA PRESISI
    • Jakarta, Indonesia
    • Data Scientist
      • Dec 2020 - Apr 2021

      • Eidara Mata Data Presisi is a geospatial based startup that engages in various government projects. The company had to monitor large government infrastructure projects using drones and CV models and produced monthly reports. • Main objectives were to conduct object detection task from aerial images and get geo location of each bounding box • Actions taken were tiling large images to smaller sizes and utilizing multiple image augmentation due to limited data(preprocessing), transfer learning with YOLOV5(model selection and hyperparameter tuning) and finally transforming pixel coordinates to geolocation using affine transformations. • Model produced mAP (0.5) > 0.9 and with much higher inference time > 10 min compared to previous model > 2 hours. Model was then integrated to the GIS team workflow Show less

Education

  • Keio University
    Policy Management, Public Policy and Data Science
    2017 - 2022

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