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.
Who are we looking for
We aren't looking for employees; we are looking for mission-driven pioneers. To thrive at Gogolook, you must meet the following standards:
Impact-Driven Architects: We require individuals who are motivated by the challenge of creating meaningful products that defend society against global fraud.
Relentless Learners: We expect you to take full ownership of your growth by actively participating in technical communities and workshops, representing Gogolook's expertise on the global stage.
Assertive Experts: We value those who bring their own professional opinions to the table, engaging in rigorous discussion to ensure we build only the most exceptional products.
Radical Collaborators: You must thrive in a culture of extreme transparency, where you are expected to consume company-wide information and actively participate in shaping our collective future.
If you feel you fit the bill, come join us!
About Trust Intelligence (TI)
Trust Intelligence is Gogolook's data product team, reporting directly to the Chief AI Officer. Our mission is to build ScamShare — Gogolook's double-sided trust intelligence platform that connects producers of scam-related signals with consumers who need structured, governed intelligence to fight fraud.
The core insight behind our work: the data isn't the product — knowledge is. Simply collecting scam reports does not make anyone safer. Real value comes from computing the probability that something is a scam, understanding how scam artefacts relate to each other, and delivering that intelligence at the speed and quality that downstream consumers require.
We own the Data Collector Service (DCS), Gogolook's core data ingestion infrastructure, and we are responsible for evolving it from a collection pipeline into the backbone of a knowledge network. We work closely with the Intelligent Systems Lab (ISL) for ML model development, Platform Services for API delivery, and the DAIRS team for data governance and compliance.
Role Mission
As a Data Research Engineer, you will be a core part of the Trust Intelligence (TI) team. Your mission is to bring the data perspective to the product and facilitate decisions driven by data. You will work with a data-centric AI approach, recognizing that personalization and model precision are enabled by superior first-party data. Because your job is to influence decisions — not just produce analysis — clear communication is at the heart of the role: the best insight has no value until it is understood and acted upon.
This is an early-career role built for growth. We are hiring at the entry-to-mid level and expect to develop your craft with us — recent graduates are genuinely welcome. At the beginning, you will be closest to the data: running well-defined analyses, testing hypotheses, preparing datasets, self-learning, and verifying your findings with teammates. As you grow, we expect you to take ownership of ambiguous problems — independently framing hypotheses from open market questions, leading advanced analysis (sensitivity, trade-off modeling), quantifying the ROI of data-expansion initiatives, mentoring newer engineers, and communicating statistical risks directly to senior leadership.
Importantly, the Data Research Engineer is a dedicated, long-term data-centric track with its own senior ladder — it is the economic and statistical conscience of the team. It is distinct from the Machine Learning Engineer role: you will partner with ML Engineers to make their models better through data, rather than build the models yourself. If your ambition is to grow deep in data — its quality, its value, and the decisions it drives — this is the right home for you.
Responsibilities
Hypothesis Testing : Translate defined product questions into testable statistical hypotheses.
Experimental Design : Design and execute A/B tests or offline validation experiments.
Statistical Analysis : Perform hypothesis testing, confidence intervals, and basic effect size and power analysis.
Dataset Engineering : Prepare datasets for testing and model training, including cleaning, filtering, and basic deduplication.
Data Governance : Ensure dataset relevance, formatting, and compliance with data policies.
Value Assessment : Estimate the marginal lift of new data sources using established methods.
Communication & Influence : Turn analysis into clear, actionable recommendations. Present findings and their limitations to technical and non-technical audiences alike, and make the case for data-driven decisions to product owners and leadership.
Documentation : Clearly document experiment design, assumptions, and results so others can understand, trust, and reproduce your work.
Qualifications
Proficiency in English , both spoken and written, sufficient to collaborate, document, and present in an English-working environment.
Bachelor's degree in Data Analytics, Statistics, Data Science, or a related quantitative field . A major or minor in Machine Learning is welcomed.
Entry to mid-level: 1-3 years of relevant experience. Exceptional recent graduates with a strong academic or project foundation are also encouraged to apply.
Exposure to data or experimental design — through coursework, internships, or projects involving A/B tests or offline validation. Deep prior experience is not required; a solid grasp of the fundamentals is.
Highly independent and self-motivated , with the ability to manage multiple task items within a team environment.
Ability to learn from mistakes and a continuous drive to seek self-improvement in new skills and technical knowledge.
Strong collaborative mindset , acting as a good team player who understands how value is delivered both efficiently and responsively.
Excellent communication skills — this is core to the role, not a nice-to-have. You can explain statistical reasoning clearly, translate complex technical knowledge into plain business language , and tailor the message to the audience, from engineers to product owners to senior leadership.
Proactive communicator who takes the initiative to voice concerns regarding technical risks, ask clarifying questions early, and contribute to the creation and reduction of tech debt.
Preferred Qualifications
Experience presenting analysis or research findings to stakeholders and influencing a decision with data.
Comfortable working with AI/ML Engineers and Product Managers in a supporting capacity to bridge the gap between research and implementation.
Skills & Competencies
Familiarity with Python (Pandas, NumPy, Scikit-learn); R, Matlab, or JavaScript are a plus.
Familiarity with SQL and data analysis.
Ability to perform hypothesis testing, confidence intervals, and basic effect size and power analysis.
Disciplined in clearly documenting experiment designs, underlying assumptions, and final results.
Strong data storytelling — able to distil analysis into a clear narrative and visualise results so a non-technical audience can act on them.
Experience with preparing datasets for training and testing, including cleaning, filtering, and basic deduplication.
Important: Please submit your resume in English.