Algorithm Engineer

Key Responsibilities

1. Risk Analytics and Fraud Detection

• Analyze platform risk scenarios, including fraudulent transactions, order manipulation, promotional campaign abuse, coordinated fraud, and operational policy violations.
• Identify suspicious behavioral patterns, risk indicators, and potential fraud schemes.
• Evaluate risk control strategy performance using metrics such as interception rate, detection precision, false positive rate, and financial losses.
• Recommend and implement improvements to risk rules, detection models, and control mechanisms in collaboration with relevant teams.

2. Full-Truckload Transportation Business Analytics

• Analyse business performance and strategies across key operational areas, including freight supply, transport capacity, truck-to-load matching efficiency, fulfilment quality, and pricing.
• Identify business opportunities, operational inefficiencies, and potential risks through data-driven analysis.
• Develop actionable recommendations to improve business performance, operational efficiency, and product capabilities.

3. Metrics Framework and Monitoring

• Develop and maintain key performance indicators (KPIs), risk metrics, and monitoring dashboards across multiple markets.
• Monitor business and risk indicators, identify anomalies, investigate root causes, and recommend appropriate actions.
• Establish reusable analytical frameworks and reporting processes to support consistent monitoring and informed decision-making.

4. Experimentation and Impact Evaluation

• Design and conduct A/B tests, phased rollouts, and causal impact evaluations for risk control, operational, and product strategies.
• Assess the effectiveness, costs, benefits, and potential trade-offs of different initiatives.
• Translate evaluation results into actionable recommendations to improve strategy effectiveness and decision-making quality.

5. Cross-functional Collaboration

Track implementation progress and evaluate business outcomes to ensure continuous improvement.

1. Educational Qualifications

Bachelor's degree or above in Statistics, Mathematics, Computer Science, Economics, or a related discipline.

2. Relevant Experience

• At least 2 years of relevant experience in data analytics.
• Experience in internet platforms, transaction-matching marketplaces, logistics, or financial technology (FinTech) is advantageous.
• Experience in risk analytics, fraud detection, or anti-fraud analysis is preferred.

3. Technical Competencies

• Strong proficiency in SQL and practical experience using Python for data processing and analysis.
• Good knowledge of statistical methods, hypothesis testing, and experimental design.
• Experience with big data technologies or platforms such as Max Compute, Hive, or Spark is advantageous.

4. Analytical and Business Skills

• Strong analytical thinking, problem-solving, and business understanding.
• Ability to break down complex business problems, formulate hypotheses, validate them using data, and translate findings into practical solutions.
• Ability to interpret data, identify trends and anomalies, and provide clear, evidence-based recommendations.

5. Risk Analysis Capabilities

• Strong attention to data anomalies and suspicious behavioural patterns.
• Ability to analyse user behaviour, transaction flows, and relationship networks to identify potential fraud and coordinated risk activities.

6. Language and Communication Skills

• Proficiency in written and spoken Chinese is required for day-to-day collaboration with China-based risk strategy, algorithm, and product teams, as well as for reviewing Chinese-language business documents and system materials.
• Proficiency in written and spoken English is required for cross-market collaboration, business communication, and reporting.