Carson Leung, Product Manager in Vancouver, BC, Canada
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Carson Leung

Verified Expert  in Product Management

Product Manager

Location
Vancouver, BC, Canada
Toptal Member Since
March 30, 2022

Carson is a seasoned product manager with nine years of experience designing data-intensive solutions. He developed a broad range of products, from early warning systems to high-throughput data architectures and software-as-a-service cloud components. Carson is a strategic thinker, passionate about helping businesses scale through data-driven design and automation, who has made outstanding contributions to both Fortune 500 companies and hypergrowth tech unicorns.

Project Highlights

Early Warning System
Designed and implemented an early warning system to detect performance degradation.
High-performance Analytics Database
Designed and coordinated data engineers and data scientists to deliver a high-performance database that enabled business intelligence dashboarding, advanced data-driven features, automation, and machine learning.
Predictive Behavior Analytics
Designed several upgrades for Mastercard's biometric model to enable classification of primary, secondary, and unknown user behaviors.

Expertise

Work Experience

Product Manager

2022 - 2023
Diamond Merckens Hogan, Inc
  • Worked with management to coordinate a group of three developers to rebuild a website for a multimillion construction company.
  • Developed and implemented a QA plan to ensure that we deliver a segment of the website in each sprint that addresses our end customer's business needs based on the target audience, user personas, and user journey.
  • Led the development team to adapt Agile software development best practices. Developed backlog, a triage process, and ran as Scrum Master.

Senior Data Analyst

2021 - 2022
Jumio
  • Acted as a product manager to design and deliver a high-performance serverless database for business intelligence dashboarding, data analysis, and machine learning.
  • Upgraded the company's flagship transaction monitoring product to enable financial recordkeeping and meet anti-money laundering (AML) regulations.
  • Developed the roadmap for a fraud detection model to iteratively test and optimize rule sets based on customer traffic.

Senior Data Analyst

2019 - 2021
Mastercard
  • Acted as project manager for the analytics team to deliver long-term projects in behavior analytics, anomaly detection, and database modeling for ETL.
  • Upgraded fraud detection and biometric models to classify primary, secondary, and unknown users.
  • Designed and implemented a system to automate data processing and business decision making.

Technical Product Analyst

2017 - 2019
Trulioo
  • Designed and delivered a real-time alerting system to detect anomalies and degradation in platform performance.
  • Acted as a product manager and product owner to ensure and monitor system availability and performance.
  • Oversaw and developed a data standardization layer in the company's API.
  • Designed analytic systems back end to author and automate tasks, such as data analytics, modeling, and ETLs.
  • Spearheaded POCs for various mobile, business, banking, and online risk intelligence products.

Compliance Analyst

2015 - 2016
State Street
  • Designed and implemented a system to detect insider trading activities across all APAC entities.
  • Mitigated the internal audit risk by actively analyzing the compliance project roadmap for regulatory gaps.
  • Modernized compliance controls via process reengineering.

Early Warning System

Designed and implemented an early warning system to detect performance degradation.

During Trulioo's hypergrowth phase, the engineering and customer success teams had issues keeping up with the 400+ data vendors worldwide. Each vendor had its unique maintenance schedule, downtimes, and other disruptions. The company could only react to performance degradation once it had impacted customers for a prolonged period. Often, this resulted in thousands of failed KYC and tens of thousands of dollars in lost revenue from manual onboarding.

In response, I spearheaded the design, development, and maintenance of an early warning system that proactively scans all data vendors and key clients for disruption in the company's flagship products. The system immediately notifies the customer success, engineering, and management teams with a call to action if anomalies are detected. By automatically switching affected data sources to backup data vendors, this system prevented disruptions and minimized the impact on customers. This system made our team proactive instead of reactive, which was pivotal in securing larger customer contracts and expansion.

High-performance Analytics Database

Designed and coordinated data engineers and data scientists to deliver a high-performance database that enabled business intelligence dashboarding, advanced data-driven features, automation, and machine learning.

In Jumio, the business unit I integrated was responsible for developing the Jumio transaction monitoring system and behaved like a startup within a corporation. Its database at the time was optimized for quick writing but was very suboptimal for reading and updating records. Because of this, there was a lack of business intelligence infrastructure and data consistency, which became a clear blocker for any platform-wide data analysis and building predictive models.

I designed the architecture for a high-performance, serverless database that seamlessly integrated with the existing AWS components. Because the database was serverless, it was also extremely low cost. With this database, we developed ETL pipelines for further data refinement, which enabled business intelligence dashboarding and improved data quality above the threshold for predictive modeling.

Another business-critical feature that relied on the database was the anti-money laundering incident recordkeeping, which provided metanalysis and quick fetching of customers' potential fraud incidents and enabled statistics to be reported upstream to regulatory bodies.

Predictive Behavior Analytics

Designed several upgrades for Mastercard's biometric model to enable classification of primary, secondary, and unknown user behaviors.

Mastercard had a very advanced biometric model that could derive and analyze over 400 user web behavior characteristics. Data points included device settings, geolocation, and the unique way a person types on a keyboard. Everything was combined to create a user's fingerprint, then helped determine whether a user was exhibiting a known or unknown behavior. If a user showed too abnormal behavior, its traffic would be halted via an impossible captcha. The problem was there could be multiple users within an account. If not distinguished, the behavior of numerous users would be grouped, thus significantly reducing the model's ability to tell if a behavior is abnormal.

I worked with data science and data engineering to develop a new rule set that allowed the model to recognize and segment traffic into primary, secondary, and unknown user behaviors within one account. This reduced false-positive rates in many of the company's key accounts, sometimes upward to 12%. It was eventually adopted into a primary fraud detection method in its eCommerce marketplace clients.
2009 - 2014

Bachelor's Degree in Mathematics

The University of British Columbia - Vancouver, BC, Canada

SEPTEMBER 2020 - PRESENT

AWS Certified Cloud Practitioner

AWS

JANUARY 2020 - PRESENT

Analytics Certification

Georgia Tech, via Coursera

Tools

Slack, Jira, Confluence, GitHub, R, Amazon Virtual Private Cloud (VPC), Amazon Elastic Container Service (Amazon ECS), AWS CloudFormation, Excel 2016

Platforms

Jupyter Notebook, Blockchain, Blockchain Platforms, Docker

Paradigms

Agile, Data Science, Key Performance Metrics, Requirements Analysis, Agile Product Management, Database Design, Database Schema Design, Scrum, Data-driven Design, Data-driven Development

Industry Expertise

Financial Services, Real Estate

Other

Bitbucket, Google Drive, Statistical Data Analysis, Optimization, Programming, Python 3, Amazon S3 (AWS S3), Amazon EC2, Redshift, Data Warehousing, Product Management, Databases, Dashboard Design, Data Visualization, Business Intelligence (BI), Data Analysis, Data Reporting, Data Management, Feature Backlog Prioritization, Product Roadmaps, Minimum Viable Product (MVP), Product Vision, User Experience (UX), Early-stage Startups, Project Timelines, Technology Trends, Product Strategy, Project Management, Data Science Product Manager, Predictive Analytics, Product Requirements Documentation (PRD), Product Discovery, Startups, Data Scraping, Technical Product Management, Product Consultant, Finance, Product Development, Strategy, Fintech, Cryptocurrency Wallets, Solution Architecture, Technical Consulting, Microsoft Excel, Business Strategy, Financial Modeling, Microsoft PowerPoint, Business Analysis, Data Quality, Data Cleansing, Data Cleaning, Cross-functional Collaboration, Reconciliation, Visual Studio Code (VS Code), Consumer Behavior, User Behavior, Data Analytics, Backlog Management, Stakeholder Interviews, Requirements, Project Planning, Revenue Optimization, Risk Management, Artificial Intelligence (AI), Amazon Web Services (AWS), Architecture, Web 3.0, Crypto, Cryptocurrency, Accounting, Mobile Apps, Mobile App Development, Mathematical Modeling, Machine Learning, Amazon SageMaker, AWS Lambda, Amazon QuickSight, Amazon Athena, Amazon Aurora, Dashboard Development, SQL, Data Modeling, Database Modeling, Data Product Manager, Kubernetes, Monitoring, Product Design, REST APIs, Data Engineering, Software Engineering, Automation, Data Quality Analysis, Data-driven Dashboards, User Research, Data Strategy, Statistical Modeling, Information Security, Software Requirements Specifications (SRS), Web App Development, Systems Analysis, System Requirements, IT Business Analysis, APIs, Stakeholder Management, Stakeholder Engagement, Scrum Master

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