My updated CV can be found here. This page is a spelled out version of what's listed on my CV.

Current Study & Research (2024-present)

TL, DR: Master's student in Data Science, full-ride Vingroup Scholarship, AI research intern.

I am a MSc Data Science student at Nanyang Technological University (started Aug 2024, graduating in Aug 2025). I am fortunate enough to have been fully sponsored by the Vingroup Scholarship Program for my study at NTU.

My coursework predominantly consists of data science modules (Data Mining, Data Visualization, Data Privacy, Data Systems, Data Preparation and Network Science, Probability & Statistics and my favorite, Data Science Thinking), and two machine learning classes (Machine Learning, Deep Learning). My current GPA is 4.2/5.

Concurrently, I am also working as an AI research intern at Continental-NTU Corporate Lab (since Dec 2024, expecting to finish in May 2025). I'm supervised by Assoc. Prof. Shang-wei Lin (formerly at NTU CCDS, now SIT) and Dr. Yon Shin Teo (Continental). My research interests as of April 2025 include: multimodal AI, vision-language models, image captioning, factual error detection.

Between Sep and Dec 2024, I assisted Asst. Prof. Siddharth Natarajan from Nanyang Business School in processing data for his research projects (Python, Stata), and developed a small web scraping script (Selenium).

Industry Experience (2021-2024)

TL, DR: 3-year exp DA at tech corp. (Almost) full-stack: analyses, visualizations, ELT and ML.

Before my Master's, I worked as a Data Analyst at One Mount Group from Oct 2021 until May 2024. I was recruited under the Fresh Geeks Talent Incubation Program and was mentored by Vu Hoang (now PhD Student in Information Systems, CMU). I started in the VinID analytics team (lifestyle & fintech app, 13+ million users) and then worked simultaneously in the OneHousing team (proptech) from mid 2022.

My role at One Mount revolved around three pillars: Business Intelligence (analytics & reporting), Data Engineering and Machine Learning. My tasks can be quite wide-ranging and unconventional for a Data Analyst, due to my tendency to gravitate towards more technical and experimental work.

Below is an non-exhaustive list of the projects I contributed to. I do my best to describe the goals & methodologies within privacy constraints.

Machine Learning:

(2023) VinID Customer Income Prediction (xgboost)

I leveraged our existing features store and experimented with XGBoost to classify customer into 3 income ranges (multi-class classification). The project was unsuccessful due to the lack of meaningful predictors, and time mismatch between labels (collected in 2019) and features (no data from 2019, so we used data from 2020-2022 as proxy).

(2023) VinID Voucher attributes (decision tree)

I fitted a decision tree on vouchers with high/low redemption rate and interpreted the tree to identify the most important attributes of a voucher that would affect its redemption.

(2022) Onehousing x VinID Lookalike customers (catboost)

I used a catboost model (binary classification) to identify customers that are similar to existing homebuyers, using features store from VinID. I also engineered some new features that was considered of high importance by the model. I learnt how to formalize business questions into data science problems, to diagnose the model's performance, and to automate steps in the machine learning pipelines to facilitate experimentations.

(2022) VinID Notification Interaction Prediction (catboost)

I used a catboost model to identify customers that are likely to interact with a notification. I also engineered some new features. I learnt how to quickly experiment with different model configurations and feature combinations.

(2021) VinID Winmart holiday sales prediction (Prophet)

I attempted to predict 2022 Tet holiday item-level sales using the Facebook's Prophet library. The model was unsuccessful due to the lack of representative data, as the 2021 Tet holiday was heavily affected by the COVID-19 pandemic. This is my first exposure to machine learning and time series problems.

 

Analytics Projects:

(2024) Onehousing Customer Journey Analysis

We analyzed the common paths (each step is a feature on the site) customers took after entering our website. We learnt that there was not a clear common path due to a lack of internal links between pages.

(2023) Onehousing Non-Listing Content Problem

We explored the behavior of organic users (i.e, they found our website via Google) and attempted to find patterns that would identify high-likelihood housebuyers. I led the initiative along with two other analysts, proposed ideas to track the behavior, proposed a simple metric that corresponded with high retention, and did the early exploratory analysis. I also informed the data tracking template and data warehouse design for this problem. I mentioned this project in my successful NTU application.

(2022) Onehousing x VinID Growth Project

We linked customer attributes (demographic, socio-economic, spatial data, etc.) to real estate purchasing behavior to identify key customer segments. I provided early insights that guided customer acquisition strategy, proposed data collection and A/B testing method, and built a dashboard to monitor key project metrics.

 

Data Engineering:

(2023) Onehousing Alert Engine (Python, SQL, dbt, Airflow)

I built a script that automatically detects mismatched records between two data sources and sent alerts to the Operations and Sales teams. This helps significantly reduce the time spent on data reconciliation.

(2023) Onehousing CEO Daily Update Bot (Python, SQL, dbt, Airflow)

I built a script that sends daily Slack updates to the CEO about real estate deals in OneHousing.

(2023) VinID Voucher, CRM, Ticketing Datamart (dimensional datawarehouse design)

I designed and built dimensional datamarts for the various VinID business functions, which contain data about vouchers, CRM, and ticketing. I learnt a lot about dimensional modelling and data warehouse design in the process.

(2022) VinID Data Platform Migration (BigQuery -> Dremio)

We changed our data platform and query engine from Bigquery to Dremio. I re-wrote and optimized SQL queries and data pipelines to fit the new platform. I learnt ELT best practices, most of my subsequent pipelines adhered to dbt style guide.

All dashboards/reports/models data pipelines (SQL, dbt, Airflow)

I built data processing pipelines (partially or entirely) for all projects that I was involved in.

 

Dashboards:

(2024) Onehousing Marketing Dashboard (Power BI)

We built executive dashboard for high-level metrics (acqured users, MAU, lead funnel, etc.) of the OneHousing website, with detailed analytical views for specific marketing functions. 

(2023) Onehousing Online-to-Offline Dashboard (Tableau)

I built an operational dashboard to monitor detailed lead generation and conversion activities by salespeople.

(2022) VinID Ticketing Dashboard (Superset)

I built an operational dashboard about on-app ticket sales (concerts, football matches, recreational parks etc.) and conversion funnel.

(2021) VinID OneView (web-based & Looker Studio)

We built a centralized business intelligence platform, containing company-wise key metrics for C-level executives (MTU, MAU, etc.). I built 2 high-level dashboards in 2021: Merchant (about voucher metrics such as claims/redeems) and Product (about north-star app metrics) and I took over as sole maintainer of all related data pipelines in 2022 (200+ tables).

Learning & Development Activities

TL, DR: Frequent training, mentoring, knowledge sharing on hard & soft skills.

From Sep to Nov 2024, I was a Data Analytics Mentor (teaching assistant) at MindX Technology School. I mentored a group of 10 adult first-time data learners from a diverse background in Python, SQL and Power BI lessons. I expanded on concepts taught by the main teacher, guided students through technical exercises and provided them with practical advice on pursuing a career in data.  

From Dec 2022 to May 2024, I was a Data Analytics Mentor (internal trainer) at One Mount. I built a custom curriculum and taught a 5-lesson introductory SQL course for 30+ colleagues from the Marketing and Business Development department in Dec 2022. I gave practical advice on querying large datasets (billions of rows) and analytical methods specific to our business context.

Within my data analytics team of 10+ (our population varied over the years), I led multiple training sessions on technical skills relevant to our projects: applied machine learning, machine learning explanability, inferential statistics, dimensional data warehouse design (most of which I self-learnt). Ocasionally, I shared my knowledge about soft skills, namely critical thinking and problem solving. I once delivered a presentation to the entire Data Office (~60 people from DA, DS, DE, DM teams) on the topic "Data People as Business Partners" (it was not the best presentation, but I learnt a lot about the added value of data and data people to organizations).

From Mar to Nov 2019, I was an IELTS Tutor (teaching assistant) at LinhUK IELTS School. I assisted in classes of 4 IELTS skills, working mostly with high school and university students. I graded writing assignments and provided detailed feedback on how to improve grammatical structure, lexical usage, and idea development. I also trained individually with numerous students in speaking exercise, giving practical advice on pronounciation, intonation and building reflexes to test questions.

Previous Academic Background (2015-2022)

TL, DR: Finance graduate, First-class (Highest Distinction) degree, former 2x National Prize Winner in high school.

From 2018 to 2022, I was a Finance student (Advanced Education Programs, English curriculum) at National Economics University. I graduated with a GPA of 3.71/4.0 and obtained First-class Honours (or Highest Distinction). I particularly enjoyed STEM-related courses, in which I scored full marks (Calculus, Statistics, Econometrics, Management Information Systems), as well some economics classes (Macroeconomics, International Economics). I also studied for the CFA Exam in 2021 (and quickly realized I was not suited a career in finance, so decided to follow my passion and curiosity and trasnsitioned data and tech. I got a full-time offer before taking the test (which I did not pass). 

Unrelated to NEU, but I self-studied and took the GMAT Focus Edition in Jan 2024 and scored 655 (~93% percentile, equavilent to 700-710 in the old edition).

From 2015 to 2018, I studied in English-specialized class at Bac Ninh High School for the Gifted. I was consistently in the English team competing at national and regional levels, where I received advanced lessons in all English skills, especially writing. My notable achievements include a Second Prize (2018) and a Third  Prize (2017) for English in the National Competition for Gifted Students / National English Olympiad (1). I also got a Silver Medal (2017) and a Bronze Medal (2016) for English in the Competition for Students of Gifted High Schools in the Coastal Region and Northern Delta (2). In Nov 2017, I took the IELTS test and scored 8.0 overall (9R, 8L, 7.5S, 7W).

(1) Kỳ thi chọn Học sinh giỏi Quốc gia (HSGQG), môn tiếng Anh. (2) Kỳ thi chọn học sinh giỏi các trường THPT chuyên khu vực Duyên hải và Đồng bằng Bắc Bộ, môn tiếng Anh