IOH is the 2nd biggest Telco provider in Indonesia ($ISAT). Leading Advanced Analytics and Data Science with 2 Team Members (DS and ML Engineer). Part of AI/ML/Automation Squad in SATU Pursuit of Excellence Tribe.Completed Projects: IOH Customer and Geo 360, Multi SIM Model, Home Broadband Market Insight & Penetration Strategy, Grid Level Planning Insight, Customer Persona Model, Vertex ML Model Deployment Best Practices, Datamart ETL Best Practices.Ongoing Projects: AI Transformation Project, Gen AI for Customer Care (RAG), Gen AI for Automation, Home Broadband Churn Model, IOH Prescriptive Churn Model, Network Experience Causal Inference.

IOH is the 2nd biggest Telco provider in Indonesia ($ISAT). I am in charge of Advanced Analytics, responsible for developing Advanced Analytical and ML solutions to support various stakeholders within IOH.• Customer Experience Model: Developed an ML model to understand the relationship between network parameters and customer experience, especially related to Network Complaint and NPS/CSAT survey. Identified potential silent complainers and survey detractors on subscriber level to trigger improvement plans.• SOC Network & Commercial Insight: Developed various use-cases, analysis, and dashboards to help network and commercial stakeholders from SOC point of view (Network Experience & DPI Data), through Network Experience Dashboard, Marvelous Experience Partner & Grid Dashboard, Self Service Analytics for Network & Commercial, Kabupaten & Kecamatan Focus, Network CSAT Degradation Deep Dive, etc.• FTTH Home Broadband Potential: Provide insights on high value Home Pass target area at Kecamatan & Kelurahan level based on Wifi Market Share from External Ads Exchange data, Geospatial data, SES data, and Telco behavior data.• New Site Revenue Prediction: Developed a model to predict the potential revenue of a planned new site based on surrounding site revenue, Geospatial data, SES data, and Network traffic trend.• SMB Propensity Model: Developed a model to predict the likelihood of IOH subscribers to also own small/medium business so the business team can give personalized treatment and integrate them to IDE (Indosat Digital Ecosystem) exclusives for SMBs• IOH External Data Enrichment: Led the enrichment of IOH data arsenal with 3rd party data such as Census data, POI data, Grid Population Data, Ads Exchange Data, etcTools used: Tableau, Hadoop, Impala, BigQuery, NVIDIA Cuda, Python, SQL

Segari is a Series B Online Groceries Dark Stores Startup in Indonesia. Developed an end-to-end Machine Learning solution encompassing the entire process from conceptualization to deployment.• Demand Forecasting Model: Designed and developed a demand forecasting ML model to accurately predict Segari's groceries product demand, significantly improving the fulfillment rate and reducing wastage.• Recommender System Model: Developed a recommender system for the Segari App with the objective of improving the conversion rate and Average Order Value (AOV).Tools: BigQuery, Google Cloud Function & Scheduler, Pub/Sub, Vertex AI, FastAPI, Google Cloud Build & Run

Building Scalable Analytics for GrabMart Dark Stores (Quick-Commerce Dark Store Online Groceries) • Migrated all existing Dark Stores tables and dashboards from Presto and Tableau to Azure environment.• Created, designed, and maintained the pipeline (ETL), downstream tables, reports, and dashboards in Databricks and Power BI in a way to better enable self-serve analytics within the Dark Store team.• Created Central Dashboard for high-level managements and also separate vertical dashboards for each business team (Merchandising, Ops, Supply Chain, etc)• Integrated the data from 3rd-party Odoo Warehouse Management System (WMS) and Grab database.• Created the feasibility analysis for Dark Store's new business model strategy transformation in 2022 (Quick Commerce)• Designed a Demand Forecasting Time-Series Model for SKU restocking/reordering and capacity planning. Improved the overall Item Fulfilment Rate while also halving down the Item Excess/Wastage Costs.• Designed an Item Matching Model using NLP to match our SKUs with competitors to compare pricing.• Conducted Impact Sizing and Experiment for new product developments.

Supporting ShopeePay & ShopeeFood Budget & Planning Team with Data Requests, Reports/Dashboards, and Deep Dives.• Designed and Maintained daily key metrics report & dashboard for Upper Management and Budget & Planning Team• Designed and created dashboards on order-related metrics (key stats, reliability, and cancellations)• Deep dive analysis on fulfillment (Driver Cancellations and Friday Prayer Time Analysis)• Analyzed the impact of existing MKT Campaigns on key metrics and designed A/B Tests/Experiments for upcoming MKT initiatives• Designed Machine Learning model for predicting Shopeepay user churn for CRM

MT Star Danone 2018, assigned to Sales Ops/Business Development.Google Mapping Project in Medan: initiated a transformation in Aqua's Distributor from physical mapping to digital mapping. Saved both time and fuel for delivery in addition to accelerating the acquisition of new outlets.Jugs Acceleration Project in West Java: using stores' characteristics data and sales history to shift system from manually taking orders from the store to automatically generating them. Then, improve delivery route planning by using location data in order to create better and efficient routing and distribution. Triggered 19% and 9% growth from respective projectsCategory & Channel Development: Created a new classification and service package for our big GT Customers. In addition, also proposed an initiative to win the channel.