Algorithm Engineer, Market Growth

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

About the Role

In this role, you will be responsible for developing and optimising core algorithmic models in marketing scenarios, including user behaviour prediction, profiling, ROI estimation, and traffic allocation strategies. You will design and build precision marketing models such as CTR/CVR prediction, LTV forecasting, and channel attribution analysis, leveraging user profiles, real-time behavioural data, and business objectives. A key part of the role will be creating data-driven strategies for user acquisition, retention, and repurchase, while continuously validating and improving performance through A/B testing, causal inference, and attribution analysis. You will collaborate closely with data, product, and operations teams to ensure these algorithms are effectively deployed within Binance’s marketing systems to drive measurable growth.

Responsibilities:

• Responsible for core algorithm models in marketing scenarios, including but not limited to user behavior prediction, user profiling, ROI estimation, and traffic allocation strategies, to improve marketing efficiency and conversion rates.

• Build precision marketing models (such as CTR/CVR prediction, LTV forecasting, marketing channel attribution analysis, etc.) based on user profiles, real-time behavioral data, and business objectives.

• Design data-driven algorithm strategies for user lifecycle management (acquisition, retention, repurchase) to optimize user growth and operational performance.

• Deeply understand business needs and continuously track algorithm performance; validate and optimize models through A/B testing, causal inference, and attribution analysis, ensuring ongoing iteration and business impact.