Lead Data Scientist, Growth & AI Systems

Carbon Data Solutions; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Airwallex is hiring a senior data scientist to design and deploy ML models driving global growth. Based in San Francisco, the role requires office presence three days a week and close collaboration with Growth, Marketing, Product, Sales, and Engineering.

You will build predictive models, personalization, and LLM-powered applications, plus causal inference frameworks to measure impact and inform decisions in a fast-moving setting.

Qualifications

• 7+ years in data science with a quantitative degree such as PhD or MS.
• Expert knowledge of machine learning and statistical modeling for data-driven growth strategy.
• Hands-on experience building, deploying, and maintaining ML models in production-grade systems, including cloud-based services and real-time or batch pipelines.
• Strong experience with SQL and Python.

Responsibilities

• Design, deploy, and iterate on predictive models, personalization and recommendation systems, LLM-powered applications, incentive optimization frameworks, and causal inference models with measurable business impact.
• Partner with Growth, Marketing, Product, Sales, Engineering, and other cross-functional teams to turn data and AI into clear recommendations and actionable strategies.
• Measure the causal impact of marketing and growth initiatives and translate findings into action.
• Raise the technical bar for the data science team through hands-on leadership and mentorship.

Skills

ML & modeling

Production ML

SQL

Python

Causal inference

AI agents

Leadership

Mentorship

Schema design

Education

Advanced degree (PhD/MS)

Tools

Airflow

Spark

Job description

Airwallex is hiring a senior data scientist to design and deploy ML models driving global growth. Based in San Francisco, the role requires office presence three days a week and close collaboration with Growth, Marketing, Product, Sales, and Engineering.

You will build predictive models, personalization, and LLM-powered applications, plus causal inference frameworks to measure impact and inform decisions in a fast-moving setting.