Shawn Ng
- shawn.ng.t.q@gmail.com
- shawnngtq.com
- Singapore
I am a Big Data Engineer with domain knowledge in PropTech, RegTech and FinTech. From system engineering to data engineering, I am able to deliver end-to-end data product. My speciality is building data infrastructure that powers the organization's analytics capabilities, which enables data scientists and analysts to be more effective in their functions. In addition, working at startup presented me with opportunities to lead projects that require integration of software from the software engineering team and business logic from the data team.
Work Experiences
Data Engineer
Seed; Urbanzoom is a high tech startup that utilizes artificial intelligence to value real estate properties and match homeowners or owners-to-be to the ideal real estate agent
- Managed production data infrastructure (AWS, GCP), pipelines (Airflow), models (Scikit-learn, XGBoost) and codebases (Django, Ruby on Rails, Express)
- Scraped, analyzed, cleansed, transformed and integrated all structured and unstructured data sources
- Developed, benchmarked and deployed production machine learning models and APIs using spatial temporal data
- Collaborated with internal and external teams to develop and deliver user friendly data product
Data Scientist
Series A; Regtech company that uses big data analytics and cutting edge technology to revolutionise the way companies protect themselves from criminals, terrorists and money launderers
- Completed 3 proof of concept (Docker, Bash, SQL, Jupyter, Python, Pandas, PySpark) with top financial institutes, 1 leading to contract agreement for production
- Installed, tested and provided product diagnosis while deploying software into Standard Chartered Bank production for global roll-out
- Designed and applied NLP (Textblob, SpaCy) & regex methods on text for feature extraction, leading to smarter machine learning software
- Designed classifiers (Scikit-learn) to identify outliers, leading to 100% recall
Software Engineer Intern
Seed; Y-Combinator S14 fintech startup that makes it easy for users to spend digital currencies such as bitcoin
- Built 4 dashboards (D3.js) and 12 reports on web application to analyze users’ transactions behavior, and detect potential fraud and disputed transactions
- Built new admin panel features (Angular, Ruby on Rails) to update users’ information and transferring of funds
- Analyzed 0.5M (number) of transactions and VISA invoices (ISO 8583) for accounting purposes
- Resolved 5,000+ customer technical issues and collected product feedback
Software Engineer Intern
Series B; APAC fastest growing last-mile delivery startup in the logistic space
- Analyzed 0.75M sales and shipping orders data (SQL, Redash, Excel)
- Tested and compared various JavaScript data visualization libraries (D3.js, Chart.js, Google Charts)
- Built web application dashboard (D3.js, Angular) to analyze 3000+ merchants’ daily delivery behavior
- Collaborated with software engineers to deploy code into production
Data Analytics Intern
World's largest accelerator for FinTech startups
- Scraped and cleaned data (Python, Excel) from various startup portals
- Identified and contacted high potential Fintech startups to join the accelerator program
- Analyzed social media (Facebook, Twitter) data to identify best way to engage target audience
- Provided data insights for marketing and operation team
Data Analytics Intern
Series A; Asia’s Premier Lunch Dating Company
- Built company’s analytics capabilities through testing and comparing BI tools such as Tableau, Qlik
- Created 15 dashboards (Qlik, Excel) to understand product metrics such as users growth, acquisitions, churn rate and revenue
- Derived and recommended solutions to increase user engagement and revenue while keeping acquisition cost low
- Collaborated with sales and marketing team to cross-sell and up-sell to users identified from their in-app behaviour
- Cleaned, transformed and labeled (Python, Excel) data features to improve user matching algorithms
Data Analytics Intern
World leading provider of document and knowledge management solutions
- Cleaned and standardized (Python, Excel) 6 months’ worth of data for data storage and analysis
- Generated various monthly reports to track sales, revenue and product trends
- Wrote documentations for data cleaning, transformation, storage and report generation