GTS Technology Solutions

Data Analyst

• Own the end-to-end product analytics strategy for a large-scale consumer mobile application.
• Partner closely with Product, Engineering, Marketing, and Executive Leadership to drive key business decisions.
• Analyze user behavior across the customer lifecycle, including onboarding, activation, engagement, retention, and monetization.
• Develop dashboards, reporting frameworks, and actionable insights that influence product roadmap and business strategy.
• Lead experimentation efforts, A/B testing, cohort analysis, and conversion funnel optimization.
• Define and monitor key subscription metrics including trial conversion, trial-to-paid conversion, renewal rates, LTV, retention, and user segmentation.
• Work with mobile user acquisition teams to evaluate attribution performance across AppsFlyer, Adjust, SKAdNetwork (SKAN), and other measurement platforms.
• Leverage AI/LLM tools to improve analytics workflows and business insights.
• Lead and mentor a small team while remaining hands-on with SQL and data analysis.

Head of Data Analytics – Consumer Mobile App

Frontend Engineer

• Design, build, and deploy scalable full-stack applications from concept through production
• Architect and optimize high-throughput distributed systems running on AWS
• Develop backend services using TypeScript, Node.js, Express, GraphQL, and PostgreSQL
• Build modern frontend applications with React
• Design scalable relational database schemas and optimize query performance
• Deploy and manage cloud infrastructure using Terraform and Docker
• Improve system reliability, observability, and operational excellence with an SRE mindset
• Partner with cross-functional teams to deliver secure, high-quality software
• Participate in AI-powered product development, including experimentation, evaluation, and data-driven product improvements.

Senior / Staff Full Stack Software Engineer, TypeScript

Machine Learning Engineer

• Lead the Fraud & Risk Machine Learning organization, managing a team responsible for production fraud detection models.
• Define and execute the machine learning roadmap for fraud prevention, identity verification, and risk decisioning.
• Build and scale a portfolio of production ML models from concept through deployment and continuous optimization.
• Partner with Product, Engineering, Risk, and Executive Leadership to solve complex business challenges using machine learning.
• Drive end-to-end machine learning development including: Feature engineering, Data preparation, Model development, Validation, Production deployment, Monitoring and model performance optimization.
• Establish best practices for model governance, experimentation, and production reliability.
• Mentor and grow a high-performing team of Data Scientists and Machine Learning Engineers.
• Provide technical leadership while remaining capable of contributing hands-on when necessary.
• Present technical strategy, business impact, and model performance to executive stakeholders.

Head of Machine Learning – Fraud & Risk

Recruitment

• Assist the recruitment team in managing the full-cycle recruitment process, from sourcing candidates to onboarding new hires.
• Post job openings on job boards, career websites, and social media platforms.
• Assist in reviewing resumes, screening applications, and conducting preliminary phone interviews.
• Help coordinate and schedule interviews between candidates and hiring managers.
• Maintain and update candidate records in the applicant tracking system (ATS).
• Help create and maintain recruitment materials, including job descriptions and interview guides.
• Support the team with background checks, reference checks, and other pre-employment activities.
• Assist in organizing and attending job fairs, campus recruiting events, and other sourcing activities.
• Provide administrative support to the HR team as needed.

Talent Acquisition Intern

Backend Engineer

• Build and maintain data-driven web applications using Python (backend/data engineering) and React (frontend)
• Design and develop scalable data pipelines, APIs, and frontend dashboards for healthcare workflows
• Build ETL/ELT pipelines to process clinical, operational, and healthcare datasets
• Work with healthcare data systems including EHR integrations and healthcare interoperability platforms
• Develop internal tools, analytics platforms, and operational dashboards for clinicians, administrators, and healthcare operations teams
• Integrate with healthcare data standards and systems (e.g., HL7, FHIR, Epic, Cerner environments where applicable)
• Design and optimize data models, warehouses, and data processing workflows
• Translate healthcare operational and reporting requirements into scalable data solutions
• Ensure applications and data platforms meet security, privacy, and compliance requirements (HIPAA-aligned environments)
• Monitor, debug, optimize, and maintain production data systems and applications
• Collaborate with cross-functional stakeholders including data analysts, clinicians, operations teams, and software engineers

GTS Technology Solutions

Head of Data Analytics – Consumer Mobile App

Senior / Staff Full Stack Software Engineer, TypeScript

Head of Machine Learning – Fraud & Risk

Talent Acquisition Intern

Data Engineer, Python, React

Recruiter Intern, Bilingual Mandarin