Senior Data Scientist, Algorithm (Relo to SF)

Airwallex3 months ago

Location

AU - Melbourne

Type

Full Time

Salary

USD 200,000 – 250,000

Level

Senior

Role

Data Scientist

Posted

Dec 4, 2025

Full TimeSenior

The role

Summary

Senior Data Scientist role at Airwallex focusing on growth algorithms, building predictive models and ML solutions to drive customer acquisition, engagement, and retention for their global payments platform. Requires 5+ years experience with advanced degree and expertise in production ML systems.

What you'll do

Predictive Modeling: Develop predictive models to identify high-value users for Marketing and Commercial teams

Causal Impact Analysis: Apply machine learning and statistical models to measure causal impact of marketing initiatives

Personalization Solutions: Develop personalization solutions to drive client onboarding efficiencies, optimize referral incentive impacts and product cross-selling

Churn Prevention: Develop churn models to drive growth retention strategy and customer lifecycle optimization

Cross-functional Collaboration: Partner with Marketing, Commercial, Engineering, and cross-functional teams to inform, influence, support, and execute GTM strategy and investment decisions

Strategic Communication: Communicate analysis results to executives and cross-functional teams to influence business strategy

Production ML Systems: Build, deploy, and maintain machine learning models in production-grade systems including cloud-based services and real-time pipelines

What we look for

Technical

Machine Learning ExpertiseExpert-level machine learning modeling skills to drive data-driven growth strategy

Production ML SystemsHands-on experience building, deploying, and maintaining ML models in production-grade systems

Programming LanguagesExpert proficiency in SQL for data querying and Python for scripting and ML development

Data ArchitectureExperience in schema design and dimensional data modeling

Education

Advanced DegreePhD or MS in quantitative field such as Statistics, Engineering, Sciences, Computer Science, or Economics

Experience

Industry Experience5+ years of industry experience in data science and machine learning

Executive CommunicationProven experience communicating analysis results to executives and cross-functional teams

Technology/Fintech BackgroundExperience in technology, financial services, and/or high-growth environment preferred

Skills

Required skills

Machine LearningExpert-level ML modeling for predictive analytics and growth optimization

PythonAdvanced programming skills for data science and ML model development

SQLExpert-level data querying and database management skills

Statistical AnalysisAdvanced statistical modeling and causal inference techniques

Production SystemsExperience deploying and maintaining ML models in production environments

Nice to have

Financial TechnologyDomain expertise in fintech, payments, or financial services industry

Cloud PlatformsExperience with cloud-based ML services and infrastructure

Data ArchitectureSchema design and dimensional data modeling experience

LLM SystemsExperience with Large Language Model integration and deployment

Growth MarketingUnderstanding of growth marketing strategies and customer lifecycle analytics

Compensation & benefits

Salary

USD 200,000 – 250,000 (annual)

Benefits

Global Opportunities

Access to 26 global offices and international career development opportunities

High-Growth Environment

Work at a unicorn fintech company valued at US$8 billion with accelerated learning opportunities

Relocation Support

Relocation assistance for moving to San Francisco office

Equal Opportunity

Inclusive workplace with equal opportunity employment policies

Cutting-Edge Technology

Work with latest AI and ML technologies in production financial systems

Interview process

1
Application Review — Initial screening of resume and qualifications through official careers portal
2
Technical Screen — Assessment of machine learning and statistical modeling expertise
3
Case Study — Practical data science problem solving and model building exercise
4
Executive Presentation — Present analysis results and strategic recommendations to senior stakeholders
5
Team Interviews — Multiple rounds with Growth Data Science team and cross-functional partners
6
Final Interview — Cultural fit and leadership assessment with senior management