About us
Gogolook is a leading TrustTech company founded in 2012 and listed on the Taiwan Stock Exchange in 2025 under the stock code 6902. With "Build for Trust" as its core value, the company has expanded its business from Asia to Europe and America. Gogolook’s AI technology is built on the world's largest database of digital scam data, encompassing phone numbers, websites, virtual currency wallet addresses, and other factors.
The company provides diverse anti-scam and fintech services for both consumers and businesses. Its anti-scam offerings include the digital anti-scam app "Whoscall" and a range of enterprise scam prevention solutions in combination with "ScamAdviser."
The Fintech BU empowers consumers through data-driven financial services and inclusive lending: Roo.Cash（袋鼠金融）offers transparent matchmaking for financial products like credit cards, while JUJI（招財麻吉）provides an innovative microloan service for rapid, convenient funding during urgent financial needs.
A foundation member of the Global Anti-Scam Alliance (GASA), Gogolook has also teamed up with a number of institutes such as the Taiwan National Police Agency Criminal Investigation Bureau, the Financial Supervisory Service of South Korea, Thai Royal Police, the Fukuoka city and Shibuya City government, the Philippines Cybercrime Investigation and Coordinating Center, and the Royal Malaysia Police and state government to fight scam, dedicated to creating a "scam-free environment."
Why you should join Gogolook
- Influential products: What we make are meaningful products that create values for society and defend against frauds.
- Emphasize self-growth: We encourage technical community activities, subsidize tickets for conferences and workshops so that learning is continuously supported by the company.
- Unleash your talent: We respect the professional opinions of everyone, encourage team members to discuss with each other, and make awesome products together.
- Transparent culture: We publicly share the company's information to all, every member can read and feedback, and become a part of participating in the proposal.
About the Role
Gogolook's Anti-Scam Common Services are the shared service layer behind Whoscall, ScamAdviser, and our enterprise anti-scam solutions — data ingestion, reputation, and report-handling services built on Gogolook's first-party trust data. Gogolook's anti-scam capabilities are becoming products that any agent or product can call — MCP tools and Skills — and Common Services is that surface: we turn Gogolook's models and data into services people and AI agents can integrate, trust, and rely on.
We are looking for an AI Product Lead to lead product management for these services. Your first customers are Gogolook's business units — product managers, business developers, and engineers — who need to independently onboard data sources, access reputation services, and integrate these capabilities into their own products and workflows. You make the shared capabilities easy to discover, integrate, and use through self-service interfaces, well-designed APIs, and agent-ready tools, so business units can bring AI-powered anti-scam products to their customers faster. The same APIs and MCP tools are built so that, when external developers or agents want to integrate directly, they can.
You will manage the Common Services roadmap, prioritization, delivery, and adoption within the product direction set by Gogolook's leadership, working closely with engineering, data, research, and business teams. Success means turning Gogolook's anti-scam capabilities into complete, deployable solutions — interfaces, integration contracts, and the Responsible-AI package that ships with each release — not simply shipping more AI features. Business units remain responsible for their customer-facing product decisions and commercial outcomes.
Responsibilities
Product Strategy & Roadmap: Define and manage the Anti-Scam Common Services roadmap spanning data ingestion, reputation services, shared reporting, and AI-ready interfaces. Translate business needs into measurable product priorities, balancing reusable shared-service investments against one-off requests.
Internal Customer & Service Adoption: Treat Gogolook's product managers, business developers, and engineers as your first customers. Understand how they work, turn recurring needs into reusable capabilities, and improve self-service adoption and independent task completion.
Self-Service Data Ingestion: Lead product development for source registration, data import, metadata, ingestion contracts and monitoring so business teams can contribute and manage data with minimal engineering support. Define clear contracts and safe operating paths for both people and agent-assisted workflows.
Reputation Services: Own product priorities for shared risk services covering phone numbers, URLs, content, and report handling. Ensure the services offer reliable, consistent, well-documented interfaces that products and AI agents can integrate with confidently.
AI-Ready Interfaces & Workflow Enablement: Define API and MCP tool capabilities, contracts, and integration experience for agent-facing services. Partner with internal teams to enable AI agents to securely query and update data, with clear scope, predictable behavior, and observable results. Define evaluation criteria and regression checks so agent-facing tools behave consistently across models and callers.
Service Governance & Access Control: Define product requirements for authentication, API keys, service accounts, and usage policies for Common Services interfaces. Coordinate with security, data, and legal teams on privacy, least-privilege access, and auditability, and take agent-facing services through Gogolook's Responsible-AI review before release.
Developer Experience & Integration: Package capabilities into deployable solutions — interfaces, integration contracts, documentation, and the Responsible-AI package — that business units can adapt for their own customers. Improve discovery, onboarding, API documentation, integration guidance, and usage dashboards. Reduce time to integration and replace repetitive manual requests with self-service experiences for developers and internal business users.
Cross-Functional Collaboration & Service Boundaries: Establish clear working agreements with business units, data and research teams, and platform engineering on data onboarding, access, and service ownership. Enable downstream product teams without taking over their customer-facing decisions or data investment priorities.
Product Delivery & Continuous Improvement: Own the backlog, planning, and release priorities with engineering and QA. Use adoption, integration lead time, service reliability, operational cost, and stakeholder feedback to guide continuous improvement.
Requirements
5+ years of relevant product management or comparable product ownership experience, including meaningful responsibility for a platform, API, data, or developer-facing product. Experience serving internal teams, enterprise integrators, or technical users is especially relevant.
A proven track record of owning product roadmaps and driving delivery across multiple stakeholders, balancing competing needs, setting priorities, and making clear product trade-offs.
Strong understanding of platform product management, including how to design reusable capabilities, improve developer experience, reduce integration friction, and drive adoption among internal or technical customers.
Sufficient technical fluency to work effectively with engineers on APIs, data pipelines, data contracts, schemas, authentication, authorization, and system boundaries. Able to understand technical constraints and evaluate architectural trade-offs without needing to implement the systems yourself.
Working knowledge of AI applications and agent-based workflows, including how AI agents interact with APIs, tools, and data services.
Strong analytical skills, including the ability to query and interpret data using SQL and relevant analytics tools, define meaningful product metrics, and use evidence to make decisions.
Excellent communication, documentation, and stakeholder-management skills. Able to translate business needs into clear product requirements, align technical and non-technical teams, and drive decisions without formal authority.
Full professional proficiency in Mandarin and English, with the ability to communicate effectively and produce clear product documentation in both languages.
Experience using AI tools in day-to-day work to improve research, analysis, documentation, or product operations, with sound judgment in validating their outputs.
Preferred Qualifications
Experience building or managing internal developer platforms, data platforms, API products, or shared services used by multiple teams.
Hands-on experience with the Model Context Protocol (MCP), tool calling, reusable agent skills, or workflow automation — building, integrating, or operating agent-facing tools, not only using them.
AI fluency beyond the basics: able to read evaluation output and reason about it; familiar with what happens to a model after deployment (monitoring, drift, retraining, versioning); and able to see how a model, its prompt and context, its tools, and the platform around it combine into something reliable.
Experience with data ingestion and integration, including structured and unstructured data, source registration, metadata management, and data governance.
Familiarity with the technical building blocks of shared services: authentication and authorization (API keys, OAuth, JWT, service accounts, access policies, usage metering) and cloud data infrastructure (AWS, object storage, data pipelines, workflow orchestration, data warehouses, BI dashboards).
Experience in anti-scam, trust and safety, cybersecurity, fraud prevention, identity, risk intelligence, or reputation services — especially serving enterprise customers, government agencies, or telecom operators across multiple markets.
Prior experience in technical product management, software engineering, data engineering, or solutions engineering.
