Machine Learning Engineer - Global E-Commerce Technology - Machine learning Singapore Regular

Pangleglobal; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Pangleglobal is seeking a data-driven professional to join our Machine Learning team in Singapore. You will build models and risk-focused ML solutions to support our ecommerce safety initiatives and production systems.

The role emphasizes hands-on data science with SQL, Python and R, strong analytical thinking, and the ability to translate business needs into scalable algorithms within a fast-paced environment.

Qualifications

• Bachelor or higher degrees in computer science, statistics, math, internet security or other relevant STEM majors.
• Solid hands-on data science skills. Proficiency in SQL, R and Python.

Responsibilities

• Build rules, algorithms and machine learning models to respond to and mitigate business risks in our products/platforms.
• Analyze business and security data, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions.
• Define risk control measurements. Quantify, generalize and monitor risk-related business and operational metrics.
• Support the production of scalable and optimised AI/ML models.
• Focus on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data to deploy AI/ML solutions from theoretical data science models.
• Run experiments to test the performance of deployed models and identify and resolve bugs that arise in the process.

Skills

SQL

Python

R

Education

Bachelor's degree or higher in CS/Statistics/Math/Internet Security

Job description

Team: Machine learning

Employment Type: Regular

Job Code: A247682

Responsibilities

The E-Commerce Risk Control (ECRC) team is missioned: To protect our E-Commerce users, including and beyond buyer, seller, creator; By securing the integrity of our ecommerce ecosystem and providing a safe shopping experience on the platform; Through building infrastructures, platforms and technologies, as well as collaborating with many cross‑functional teams and stakeholders. The ECRC team works to minimize the damage of inauthentic behaviors on our E-Commerce platforms, covering multiple classical and novel community and business risk areas such as account integrity, incentive abuse, malicious activities, brushing, click‑farm, information leakage etc. In this team you'll have a unique opportunity to have first‑hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy‑safe, secure and product‑friendly systems and solutions. Our challenges are not some regular day‑to‑day technical puzzles – You'll be part of a team that's developing novel solutions to first‑seen challenges of a non‑stop evolvement of a phenomenal product eco‑system. The work needs to be fast, transferable, while still down to the ground to making quick and solid differences.

• Build rules, algorithms and machine learning models to respond to and mitigate business risks in our products/platforms. Such risks include and are not limited to account integrity, scam, deal‑hunter, malicious activities, brushing, click‑farm, information leakage etc.
• Analyze business and security data, uncover evolving attack motion, identify weaknesses and opportunities in risk defense solutions, explore new space from the discoveries.
• Define risk control measurements. Quantify, generalize and monitor risk‑related business and operational metrics. Align risk teams and stakeholders on risk control numeric goals, promote impact‑oriented, data‑driven data science practices for risks.
• Support the production of scalable and optimised AI/ML models.
• Focus on building algorithms for the extraction, transformation and loading of large volumes of real‑time, unstructured data to deploy AI/ML solutions from theoretical data science models.
• Run experiments to test the performance of deployed models and identify and resolve bugs that arise in the process.

Qualifications

Minimum Qualifications:

• Bachelor or higher degrees in computer science, statistics, math, internet security or other relevant STEM majors (e.g. finance if applying for financial fraud roles).
• Solid hands‑on data science skills. Proficiency in statistical analytical tools, such as SQL, R and Python.

Preferred Qualifications:

• Familiarity with machine learning or social/content online platform analytics. Bonus given to proficiency in modern machine learning applications.
• Ability to think critically, objectively, rationally. Reason and communicate in result‑oriented, data‑driven manner. High autonomy.
• Experience in LLM/Agent technology is a plus.