Data Scientist, Market Growth (Lifecycle)

About the role

In this role, you will be responsible for developing algorithm models that predict user behavior and optimize marketing efficiency. Your work will directly impact user growth and operational performance by designing data-driven strategies for user lifecycle management.

You will collaborate with various teams, including data, product, and operations, to implement these algorithms effectively. Success in this position requires a solid understanding of marketing scenarios and the ability to validate models through rigorous testing.

The salary range for this role is between $120,000 and $200,000, reflecting the expertise required.

Skills that matter here

• Python: This role requires proficiency in Python for developing and optimizing algorithmic models.
• Scala: Scala is one of the programming languages used for big data processing in this position.
• Java: Java may be utilized in various algorithm implementations and data processing tasks.
• Hadoop: Hadoop is part of the big data processing framework knowledge necessary for this role.
• Spark: Experience with Spark is essential for handling large-scale data in marketing algorithms.
• TensorFlow: TensorFlow is one of the tools used for building machine learning models in this position.

Who this role suits

• A candidate with a strong analytical mindset who thrives on solving complex problems.
• Someone with a collaborative spirit who enjoys working with diverse teams.
• An individual who is self-motivated and can work independently while also contributing to team goals.
• A professional with a solid foundation in algorithms and programming, particularly in a marketing context.

From the employer

• 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.
• Collaborate closely with data, product, and operations teams to drive the implementation of algorithms in marketing systems (e.g., user growth initiatives).

• At least 3 years of experience in related fields, with a solid foundation in algorithms and programming.
• Master’s Degree or above in Computer Science, Statistics, Applied Mathematics, Machine Learning, or related disciplines.
• Proficient in at least one programming language such as Python, Scala, or Java; familiar with big data processing frameworks like Hadoop, Spark, or Flink.
• Strong understanding of machine learning algorithms (e.g., LR, GBDT, DNN), commonly used models in marketing scenarios (e.g., Uplift Model, Lookalike), and tools such as TensorFlow or PyTorch.
• Familiar with SQL/Hive and experienced in large-scale data cleaning, feature engineering, and model tuning.
• Deep understanding of user growth, ad placement, and e-commerce marketing scenarios, with the ability to design algorithm solutions aligned with business goals.
• Clear logical thinking, ability to work independently, and strong team collaboration and communication skills.

• Shape the future with the world’s leading blockchain ecosystem
• Collaborate with world-class talent in a user-centric global organization with a flat structure
• Tackle unique, fast-paced projects with autonomy in an innovative environment
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• Competitive salary and company benefits
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)