Senior Data Scientist, Algorithm - Financial Markets

Airwallex1 months ago

Location

US - San Francisco

Type

Full Time

Salary

USD 200,000 – 250,000

Level

Senior

Role

Data Scientist

Posted

Jan 29, 2026

Full TimeSenior

The role

Summary

Senior Data Scientist role at Airwallex focused on financial markets risk management and algorithm development. The position requires expertise in statistical modeling and machine learning to optimize FX trading, market forecasting, and risk assessment for a global fintech platform serving 200,000+ businesses.

What you'll do

Risk Portfolio Management: Effectively manage Airwallex's comprehensive risk portfolio and deliver actionable insights to shape overall risk strategy within global financial markets

Statistical Modeling & ML Implementation: Apply advanced statistical and machine learning models to estimate financial markets risk exposures including FX, credit, capital, and product-specific risks

Market Optimization: Optimize market forecasting algorithms, order routing systems, fund efficiency metrics, and dynamic pricing decisions using data-driven approaches

Cross-Functional Collaboration: Partner with Product Managers, Traders, Risk teams, Engineering, and stakeholders to deliver data-driven recommendations supporting commercial objectives

Executive Communication: Present analytical results and insights clearly to cross-functional teams and executives to influence business strategies and decision-making processes

Algorithm Development: Design and implement sophisticated algorithms for real-time risk assessment and automated trading decision support systems

What we look for

Technical

Programming ExpertiseExpert-level proficiency in SQL for data querying, Python for scripting and analysis, and statistical software like R for quantitative modeling

Machine Learning & StatisticsStrong ability to perform statistical analysis and develop machine learning models for data-informed financial decisions and risk assessment

Schema DesignExperience in database schema design and dimensional data modeling for financial data architecture and optimization

Financial Markets KnowledgeStrong understanding of financial markets, foreign exchange mechanisms, and dynamic trading systems architecture

Education

Advanced Degree RequirementPhD or Master's degree in quantitative fields such as Statistics, Engineering, Sciences, Computer Science, or Economics

Quantitative BackgroundStrong foundation in mathematical modeling, statistical analysis, and computational methods for financial applications

Experience

Industry ExperienceMinimum 5+ years of professional experience in data science, quantitative analysis, or related analytical roles in technology or financial services

Executive CommunicationProven experience communicating complex analytical results to executives and cross-functional teams to influence strategic decisions

Financial Services BackgroundPreferred experience in technology companies, financial services, trading environments, or high-growth startup ecosystems

Skills

Required skills

Python ProgrammingExpert-level Python skills for data analysis, machine learning model development, and automation of financial processes

SQL MasteryAdvanced SQL proficiency for complex data querying, joins, and database optimization in financial data environments

Statistical AnalysisStrong statistical modeling skills including hypothesis testing, regression analysis, and time series forecasting for financial markets

Machine LearningHands-on experience with supervised/unsupervised learning, feature engineering, and model validation for risk assessment

Financial Markets KnowledgeDeep understanding of FX markets, trading systems, risk management, and financial instrument pricing mechanisms

Executive CommunicationAbility to translate complex technical findings into clear, actionable insights for business stakeholders and executives

Nice to have

R ProgrammingStatistical computing experience with R for advanced quantitative analysis and specialized financial modeling packages

Schema DesignExperience with database schema design and dimensional modeling for optimized financial data architecture

Trading ExperienceBackground in algorithmic trading, order management systems, or electronic market making in financial services

Fintech ExperiencePrevious work in financial technology companies, payment platforms, or high-growth startup environments

Cloud PlatformsExperience with AWS, GCP, or Azure for scalable data processing and machine learning model deployment

Compensation & benefits

Salary

USD 200,000 – 250,000 (annual)

Benefits

Global Career Growth

Opportunity to work across 26 offices globally with a diverse team of 2,000+ innovative professionals in the fintech space

Equity Participation

Equity participation in a US$8 billion valued company backed by world-leading investors including T. Rowe Price, Visa, and Mastercard

Professional Development

Accelerated learning opportunities working on complex, high-visibility problems with exceptional teammates in cutting-edge fintech

Hybrid Work Model

Flexible hybrid work arrangement combining remote work with collaborative in-office time in San Francisco

Equal Opportunity Environment

Inclusive workplace committed to diversity and equal opportunity employment with accommodation support for disabilities

Innovation Culture

Work environment that encourages AI adoption, first-principles thinking, and zero-to-one product development

Interview process

1
Initial Screening — Phone or video screening with HR to discuss background, experience, and role alignment with focus on quantitative experience
2
Technical Assessment — Take-home coding challenge involving financial data analysis, statistical modeling, or machine learning problem solving using Python/SQL
3
Technical Interview — Deep-dive technical discussion covering statistical methods, machine learning approaches, and financial markets knowledge with data science team
4
Case Study Presentation — Present a business case study demonstrating analytical thinking, communication skills, and ability to derive actionable insights from data
5
Cross-Functional Panel — Panel interview with stakeholders from Product, Engineering, and Risk teams to assess collaboration and business acumen
6
Final Leadership Interview — Leadership interview focusing on cultural fit, career motivations, and strategic thinking with senior data science or executive team members