# Staff Data Scientist, Algorithm (Risk Product – AML & Financial Crime) **Company:** [Airwallex](https://scaleengineer.com/companies/airwallex) Staff Data Scientist, Algorithm role at Airwallex leading the development of AI-powered financial crime detection and anti-money laundering (AML) capabilities. This is a technical leadership position responsible for building and scaling advanced risk detection systems including name screening, entity resolution, network intelligence, and machine learning models to identify and mitigate suspicious activity across payment transactions. Requires 7+ years of applied machine learning expertise with demonstrated impact in data science or related quantitative fields, plus hands-on experience with Python, SQL, and AI-powered risk detection frameworks relevant to financial crime prevention in regulated environments. **Role:** Staff Data Scientist **Seniority:** Staff **Locations:** SG - Singapore **Salary:** 180000–280000 USD [Apply](https://jobs.ashbyhq.com/airwallex/3644405e-f350-4a3a-bb53-54bfb9400efe) Canonical: https://scaleengineer.com/jobs/airwallex/staff-data-scientist-algorithm-risk-product-aml-financial-crime --- ## Responsibilities - Lead AML and Financial Crime Data Science Strategy: Own the overarching data science strategy for anti-money laundering and financial crime risk controls across the Risk Product portfolio. Define roadmap priorities, set technical standards, and ensure alignment between data science initiatives and regulatory compliance requirements. Drive adoption of advanced machine learning methodologies across the risk detection framework. - Develop Proactive Risk Detection Systems: Build and scale machine learning systems that proactively identify emerging threats, suspicious behavioral patterns, control gaps, and previously unknown financial crime risks. Move beyond reactive rule-based systems to develop predictive algorithms that detect coordinated activity, fraud rings, and money laundering networks before they cause harm. - Advance AI-Powered Screening and Network Intelligence: Architect and improve advanced risk capabilities including AI-powered customer and counterparty screening, graph-based network intelligence, entity resolution for name matching across data sources, and machine learning models for behavioral anomaly detection. Implement LLM-based solutions for adverse media monitoring and risk signal extraction. - Build Transaction Monitoring and AML Use Cases: Develop and operationalize critical AML use cases including real-time transaction monitoring, name screening systems, PEP and sanctions list matching, and detection of coordinated activity across account hierarchies. Design machine learning models to identify structured layering patterns, circular transaction flows, and collusive behavior indicative of financial crime. - Cross-Functional Collaboration and Production Deployment: Partner closely with Risk Product managers, Software Engineers, Financial Crime Compliance teams, and Risk Operations to translate complex risk problems into production-grade, scalable controls. Bridge the gap between research and operations by ensuring models remain effective at production scale, can be monitored in real-time, and integrate seamlessly with existing risk infrastructure. - Technical Leadership and Mentorship: Elevate the technical bar across the Risk Data Science team through architectural leadership, reusable frameworks, best practices documentation, and direct mentorship of junior and mid-level data scientists. Build institutional knowledge around financial crime detection patterns and establish standards for model validation, bias testing, and explainability in regulated environments. ## Requirements ### education - {"name":"Bachelor's Degree in Quantitative Field","description":"Bachelor's degree or equivalent in Computer Science, Mathematics, Statistics, Physics, Engineering, Economics, or related quantitative discipline. Foundational understanding of algorithms, data structures, and computational complexity."} - {"name":"Advanced Degree Preferred","description":"Master's degree or PhD in Computer Science, Machine Learning, Statistics, or related field is preferred but not required if compensated by exceptional professional experience and demonstrated expertise."} ### technical - {"name":"Python","description":"Expert-level proficiency in Python for data analysis, machine learning model development, and production engineering. Comfortable with scientific computing libraries (NumPy, Pandas, Scikit-Learn) and modern deep learning frameworks (PyTorch, TensorFlow)."} - {"name":"SQL","description":"Strong SQL expertise for complex data querying, aggregation, and analysis across large datasets. Ability to optimize queries for performance and design efficient data schemas for machine learning feature engineering."} - {"name":"Machine Learning and AI Frameworks","description":"Deep expertise with modern ML frameworks including supervised learning (classification, regression), unsupervised learning (clustering, anomaly detection), graph neural networks, and large language model (LLM) fine-tuning and evaluation."} - {"name":"Data Engineering and Infrastructure","description":"Practical knowledge of data warehousing, ETL pipelines, stream processing for real-time data, and working with cloud-based infrastructure. Experience designing feature stores, managing data quality, and optimizing computation for model training and inference."} - {"name":"Statistical Analysis and Experimentation","description":"Strong foundation in statistical methods including hypothesis testing, causal inference, A/B testing design, and power analysis. Ability to establish baselines, measure impact rigorously, and validate assumptions before deployment."} ### experience - {"name":"7+ Years in Data Science or Machine Learning","description":"Demonstrated career progression with significant impact at Staff, Lead, or equivalent scope in data science, machine learning, applied AI, or risk analytics roles. Track record of shipping production systems and influencing technical direction across organizations."} - {"name":"Applied Machine Learning at Scale","description":"Proven expertise building, productionizing, and maintaining machine learning systems at scale. Experience architecting end-to-end ML pipelines, managing model drift, implementing monitoring frameworks, and iterating based on production performance metrics in high-stakes environments."} - {"name":"Financial Crime Detection or Risk Domain","description":"Hands-on experience in at least one area critical to financial crime detection: NLP/large language models for text analysis, entity resolution for identity matching, information retrieval, graph analytics for network detection, anomaly detection algorithms, or risk scoring frameworks."} - {"name":"Cross-Functional Leadership Experience","description":"Demonstrated ability to lead without formal authority, influencing product strategy, engineering timelines, and compliance requirements. Experience navigating ambiguity, building consensus across technical and non-technical stakeholders, and driving complex initiatives to completion."} ## Skills ### required - {"name":"Large Language Models and NLP","description":"Experience building or fine-tuning large language models, prompt engineering, retrieval-augmented generation (RAG), and evaluating LLM outputs for information extraction and risk signal discovery in financial crime detection."} - {"name":"Entity Resolution and Name Matching","description":"Expertise in techniques for matching and resolving entities across disparate data sources, handling name variations, fuzzy matching, and probabilistic matching at scale. Experience building name screening systems or adverse media extraction pipelines."} - {"name":"Graph Analytics and Network Analysis","description":"Proficiency with graph databases and analytics tools for detecting patterns in networked data. Ability to build graph neural networks or apply community detection algorithms to identify money mule networks, collusive behavior, and coordinated high-risk entities."} - {"name":"Anomaly Detection and Unsupervised Learning","description":"Strong background in anomaly detection techniques including isolation forests, local outlier factors, autoencoders, and statistical approaches. Experience defining normal behavior baselines and detecting deviations for financial crime signals."} - {"name":"Risk Scoring and Classification","description":"Experience developing risk scoring models, calibrating thresholds, and designing classification systems that balance false positive rates against detection sensitivity. Knowledge of regulatory requirements for explainability and monitoring."} ### preferred - {"name":"Fintech and Regulated Finance Experience","description":"Background in fintech, payments platforms, banking, or other regulated financial services. Understanding of compliance frameworks, data governance, and the unique challenges of building risk systems in regulated environments."} - {"name":"AML and Financial Crime Compliance","description":"Direct experience in anti-money laundering, financial crime prevention, sanctions screening, PEP (politically exposed person) identification, adverse media monitoring, fraud prevention, or transaction monitoring. Familiarity with regulatory standards (AML/KYC compliance)."} - {"name":"LLM Evaluation and Observability","description":"Hands-on experience evaluating large language model quality, designing model and prompt experimentation frameworks, assessing retrieval quality in RAG systems, and implementing AI observability platforms for monitoring model performance in production."} - {"name":"Trust and Safety or Marketplace Intelligence","description":"Experience in trust and safety, marketplace intelligence, or abuse prevention at consumer or B2B platforms. Knowledge of coordinated inauthentic behavior detection, synthetic identity fraud, or account takeover prevention."} - {"name":"Feature Engineering for Time-Series Data","description":"Advanced skills in extracting predictive features from temporal transaction data, behavioral signals, and sequential patterns. Experience with recurrent neural networks, attention mechanisms, or transformer architectures for time-series analysis."} ## Tech stack ### tools - {"name":"AWS or Google Cloud Platform","description":"Cloud computing platforms for model training, inference serving, and data pipeline orchestration. Experience with managed services like SageMaker or Vertex AI."} - {"name":"Airflow or dbt","description":"Workflow orchestration and data transformation tools for building reliable, maintainable data pipelines and feature engineering workflows."} - {"name":"Jupyter or VS Code","description":"Development environments for iterative analysis, experimentation, and model development with version control integration."} - {"name":"MLflow or Weights & Biases","description":"Experiment tracking and model management platforms for reproducibility, hyperparameter optimization, and production deployment workflows."} - {"name":"Git and version control","description":"Essential for collaborative development, code review, and maintaining reproducible machine learning codebases."} ### others - {"name":"Statistical Methods and Causal Inference","description":"Strong foundation in statistical testing, hypothesis validation, causal inference techniques, and A/B testing design for rigorous evaluation of model impact."} - {"name":"Model Explainability and Interpretability","description":"Knowledge of techniques for making black-box models interpretable (SHAP, LIME), crucial for regulatory compliance and gaining stakeholder trust in risk decisions."} - {"name":"Real-time Processing and Stream Computing","description":"Experience with streaming data frameworks (Kafka, Spark Streaming) for building real-time detection systems and processing high-velocity transaction data."} - {"name":"API Development and Deployment","description":"Familiarity with building REST APIs, containerization (Docker), and deploying models as microservices for integration with production risk platforms."} ### databases - {"name":"PostgreSQL or MySQL","description":"Relational databases for structured transaction data, customer information, and feature stores. Knowledge of query optimization, indexing, and handling large-scale data."} - {"name":"Snowflake or Redshift","description":"Cloud data warehouses for handling massive analytical workloads, distributed queries, and real-time data processing across the organization."} - {"name":"Neo4j or ArangoDB","description":"Graph databases for storing and querying relationship data, enabling efficient network analysis and detection of complex financial crime patterns."} - {"name":"Elasticsearch or Solr","description":"Search and indexing engines for information retrieval, full-text search across adverse media databases, and rapid querying of risk indicators."} ### languages - {"name":"Python","description":"Primary programming language for model development, data analysis, feature engineering, and algorithm implementation. Experience with Jupyter notebooks for exploratory analysis and production-grade Python for scalable solutions."} - {"name":"SQL","description":"Essential for querying data warehouses, writing complex aggregations, and designing efficient data retrieval patterns for machine learning pipelines."} - {"name":"R","description":"Optional language for statistical analysis, specialized machine learning packages, and exploratory data analysis, particularly for statistical modeling and visualization."} ### frameworks - {"name":"PyTorch or TensorFlow","description":"Deep learning frameworks for developing neural networks including LLM fine-tuning, graph neural networks, autoencoders, and recurrent networks for sequence analysis and anomaly detection."} - {"name":"Scikit-Learn","description":"Comprehensive machine learning library providing algorithms for classification, regression, clustering, and anomaly detection. Foundation for traditional ML approaches in risk detection."} - {"name":"LangChain or similar LLM frameworks","description":"Tools for building applications with large language models, including prompt management, retrieval-augmented generation (RAG), and chaining multiple LLM calls for complex information extraction."} - {"name":"XGBoost or LightGBM","description":"Gradient boosting frameworks for building high-performance classification and regression models at scale, commonly used for risk scoring and anomaly detection systems."} - {"name":"NetworkX or PyG (PyTorch Geometric)","description":"Graph analysis and graph neural network libraries for network intelligence, community detection, and relationship analysis in financial crime networks."} ## Benefits ### benefits - {"name":"Equity and Stock Options","description":"Meaningful equity stake in Airwallex, an $11B valuation fintech company backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Sequoia, and Salesforce Ventures. Opportunity to benefit from company growth trajectory."} - {"name":"Professional Development and Learning","description":"Access to cutting-edge AI and machine learning resources, conference attendance, online courses, and mentorship from industry experts. Annual learning and development budget for advancing expertise in financial crime detection and AI/ML technologies."} - {"name":"Collaborative Global Environment","description":"Work with the brightest data scientists, engineers, and product specialists across 27 global offices. Access to diverse expertise in payments, fintech, compliance, and risk management across international markets."} - {"name":"High-Impact Work","description":"Direct influence on financial crime prevention at a global scale, protecting 250,000+ businesses and driving regulatory compliance. Work on problems that have tangible security and financial implications for millions of transactions."} - {"name":"Health and Wellness Benefits","description":"Comprehensive health insurance, mental wellness programs, flexible work arrangements, and fitness/wellness benefits. Support for maintaining work-life balance while working on ambitious technical challenges."} - {"name":"Flexible Work Arrangements","description":"Remote work options with flexibility to work from various global locations. Access to collaboration spaces and equipment to support productive remote and hybrid work."} - {"name":"Parental Leave and Family Support","description":"Generous parental leave policies, adoption support, and family-friendly benefits. Recognition of diverse family structures and life circumstances."} - {"name":"Visa and Relocation Assistance","description":"Support for international candidates including visa sponsorship and relocation packages for candidates joining from outside primary office locations."} ## Compensation - **max:** 280000 - **min:** 180000 - **currency:** USD - **stockOptions:** true ## Interview process ### steps ## Full description ## **About Airwallex** Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 250,000 businesses worldwide – including Brex, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale. Proudly founded in Melbourne, we have a team of over 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us. ## **Attributes We Value** We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you’re motivated by our mission and [operating principles](https://www.airwallex.com/us/operating-principles). You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor. You're humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end-to-end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next. ## **What you’ll do** As a Staff Data Scientist in the Risk Product Data Science team, you will be a technical leader responsible for advancing how Airwallex proactively identifies and mitigates financial crime risk. You will work across customer, transaction, behavioral, network and external intelligence data to detect known and emerging risks, uncover suspicious communities and coordinated activity, and improve how we screen customers and counterparties for financial crime exposure. A key part of the role is developing AI-powered risk detection capabilities, including name screening, entity resolution, network intelligence, and proactive discovery of previously unknown risk patterns. You will partner closely with Risk Product, Engineering, Financial Crime Compliance and Risk Operations to translate complex risk problems into scalable, production-grade controls. ## **Responsibilities** * Lead the Data Science strategy for AML and Financial Crime risk controls across Risk Product. * Develop proactive risk detection to uncover emerging threats, suspicious behaviors, control gaps and previously unknown financial crime patterns. * Develop and scale advanced risk capabilities across AI-powered screening, graph and network intelligence, entity resolution, and machine learning. * Advance key AML/FCC use cases including name screening, transaction monitoring, and detection of coordinated activity and fraud or financial crime rings. * Partner with Risk Product, Engineering, Financial Crime Compliance and Risk Operations to translate complex risk problems into effective, scalable production controls. * Influence the Risk Product roadmap and raise the technical bar for Risk Data Science through technical leadership, reusable frameworks, and mentoring. ### **Minimum Requirements** * 7+ years of experience in Data Science, Machine Learning, Applied AI, Risk Analytics, or a related quantitative field, with demonstrated impact at Staff, Lead, or equivalent scope. * Strong expertise in applied machine learning and modern AI, with experience building and productionizing data-driven solutions at scale. * Experience with one or more areas highly relevant to financial crime detection, such as NLP/LLMs, entity resolution, information retrieval, graph or network analytics, anomaly detection, or risk scoring. * Strong SQL and Python skills are must * Strong communication and stakeholder management skills, with the ability to influence senior partners across Product, Engineering, Risk, Operations, and Compliance. ## **Preferred qualifications** * Experience in fintech, payments, banking, marketplaces or another regulated or high-risk domain. * Experience in AML, financial crime, sanctions, PEP screening, adverse media, fraud, trust and safety or transaction monitoring. * Experience building or improving name screening or adverse media systems. * Familiarity with modern LLM evaluation, prompt/model experimentation, retrieval quality assessment and AI observability. * Experience applying graph analytics to detect mule networks, collusive behavior, coordinated account activity or interconnected high-risk entities. * Experience developing proactive risk discovery capabilities rather than exclusively optimizing existing supervised models or rules. ## **Applicant Safety Policy: Fraud and Third-Party Recruiters** _To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @_[_airwallex.com_](http://airwallex.com) _email address. Please apply only through_ [_careers.airwallex.com_](http://careers.airwallex.com) _or our official LinkedIn page._ _Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary._ ## **Equal opportunity** Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.