Risk Analyst

Lalamove is disrupting the logistics industry by connecting customers and drivers directly through our technology. We offer customers a lightning fast and convenient way to book delivery and moving services whether they are at their home, at work or on the go. People talk about O2O, we live it!

Currently, Lalamove is a leading global on-demand delivery platform with millions of delivery partners serving millions of orders everyday. With 1600+ employees spread across SEA and LATAM, our 10-year old company has reached unicorn status in 2018, is well funded by prominent VCs and has kept growing at tremendous speed since.

Our strength lies in our internal values, namely Passion in serving local communities, empowering SMEs and our driver partners, Execution and Grit because that is how we differentiate ourselves by never giving up and striving for excellence, and Humility - awareness in ourselves to learn from others and never stop improving.

At Lalamove, we strongly believe in the power of community. Millions of drivers and customers use our technology every day to connect with one another and move things that matter. Delivery is what we do best and we ensure it is always fast and simple. Since 2013, we have tackled the logistics industry head on to find the most innovative solutions for the world’s delivery needs.

Our vision is to bring communities closer and make city life easier by allowing fast and convenient circulation of goods. We realize this vision with a ‘glocal’ approach, building a robust operations team to adapt our product to local networks of businesses and delivery contractors. At the same time, we have ambition to build an international brand by establishing an even more global presence.

Lalamove is looking for an experienced Risk Analyst to support our Risk team in Hong Kong. The role entails driving analytics insights and enabling senior leadership to make effective, analytically driven decisions, in particular areas of fraud risk and financial efficiency.

The ideal candidate should be passionate about logistics and e-commerce, has a strong analytical and consultative mindset, deep understanding of risk strategy methodology and design, the ability to thrive in a dynamic, fast-paced environment delivering against tight deadlines, and a passion for scaling operations through automation.

What you’ll do

• Carry out detailed walkthroughs and process mapping to identify significant risks and controls in Lalamove’s systems
• Monitor, review, and communicate the impact to the Risk team of future initiatives, including new products, changes to existing products, and major business initiatives
• Collaborate with internal stakeholders like customer support, marketplace, business development, data science, engineering, payments, and product to launch and monitor fraud prevention controls
• Review the financial impact of fraud incidents, understand root cause, and design preventative controls
• Design, evaluate, test, and implement risk rules to counteract fraud patterns and reduce fraud losses
• Regularly review and analyze the effectiveness of rules and make adjustments or deprecations as needed
• Be intensely data driven in prioritizing and evaluating the results of all work
• Be bold! Develop insights using data and research to create a risk control system that deters fraudulent behavior while providing a positive experience for Lalamove users.

What we seek

• Proficient in SQL
• Proven track record of applying analytical/statistical methods to solve real-world problems using data
• Mandarin speakers are preferred as this role will be working closely with Hong Kong and China counterparts.
• Organized and self-motivated individual, comfortable operating within ambiguity
• Strong communication skills required to operate across multiple departments and stakeholders
• Desire to continually learn, adapt and work in a fun, fast-paced environment

What you’ll need

• 3+ years in Risk/Fraud/Payments focused role
• BA/BS degree in Mathematics, Statistics, Computer Science, Finance, Economics or other quantitative field