# Staff Data Scientist **Company:** [Airwallex](https://scaleengineer.com/companies/airwallex) Staff Data Scientist role at Airwallex, a leading AI-native financial operating system serving over 675,000 businesses globally. This position combines technical leadership with strategic influence, requiring 8+ years of quantitative analysis expertise and a track record of mentoring data science teams. You'll architect scalable decision-support systems and AI-driven product optimization across a regulated fintech platform operating in 27 offices worldwide. **Role:** Staff Data Scientist **Seniority:** Staff **Locations:** UK - London **Salary:** 180000–240000 GBP [Apply](https://jobs.ashbyhq.com/airwallex/0778c237-c5f3-4572-b732-fa1fdcae3022) Canonical: https://scaleengineer.com/jobs/airwallex/staff-data-scientist-0778c237 --- ## Responsibilities - Technical Leadership and Team Development: Provide hands-on technical mentorship to data scientists, review complex analytical work, establish coding standards and best practices, and define the team's technical roadmap. Guide team members through ambiguous problems, develop their analytical rigor, and foster a culture of scientific thinking and continuous learning. - Strategic Decision Support Systems: Partner with cross-functional executives to translate business strategy into analytical frameworks. Build systems that answer critical questions repeatedly, measure organizational performance against key metrics, identify emerging trends, and provide reliable forecasts that inform investment decisions and long-term planning. - Quantitative Analysis and Forecasting: Apply advanced statistical methods, data mining techniques, and forecasting models to analyze complex business questions. Conduct rigorous hypothesis testing, control for confounding variables, and deliver data-driven insights that reveal patterns invisible to conventional analysis. - Data Storytelling and Influence: Transform technical findings into compelling narratives tailored to executive and cross-functional audiences. Present complex analyses with clarity and conviction, highlight business implications, anticipate stakeholder questions, and influence strategic direction through evidence-based reasoning. - Scalable Analytical Systems Architecture: Architect robust systems that enable reliable, repeated decision-making at organizational scale. Build semantic layers, context retrieval systems, validation gates, and governance frameworks that ensure analytical outputs remain trustworthy, contextualized, and operationally sound as complexity increases. - Machine Learning Product Optimization: Develop ML systems that directly optimize business outcomes including ranking algorithms, pricing models, risk assessment, customer experience personalization, and resource allocation. Extend beyond model training to design intelligent systems that reason, use tools, operate within guardrails, and measure incremental impact rigorously. - Experimental Design and Impact Measurement: Design and analyze A/B tests and causal experiments to measure the incremental impact of new features, strategies, and systems. Identify and control for confounding factors, challenge misleading conclusions, and establish rigorous standards for statistical significance and practical business value. - Key Metric Definition and Monitoring: Define goal-aligned KPIs and performance indicators across the organization. Establish automated monitoring dashboards, identify anomalies early, alert teams to performance shifts, and provide context around changing metrics to support proactive decision-making. ## Requirements ### education - {"name":"Advanced Degree in Quantitative Field","description":"PhD or Master's degree in Statistics, Engineering, Sciences, Computer Science, Economics, Mathematics, or related quantitative disciplines."} ### technical - {"name":"Data Querying Languages","description":"Expert-level SQL for complex analytical queries, performance optimization, and working with large transactional datasets in distributed systems."} - {"name":"Programming Languages","description":"Advanced proficiency in Python and/or R for statistical computing, data manipulation, machine learning model development, and analytical reproducibility."} - {"name":"Machine Learning Systems","description":"Hands-on experience building end-to-end ML systems including feature engineering, model selection, hyperparameter tuning, evaluation, and production deployment."} - {"name":"Data Infrastructure","description":"Understanding of data pipelines, data warehousing, ETL processes, and working effectively with data engineering teams to access clean, reliable data sources."} - {"name":"Experimentation and Causal Inference","description":"Expertise designing and analyzing randomized experiments, understanding confounding variables, and applying causal inference methods to estimate true treatment effects."} ### experience - {"name":"8+ Years Industry Data Science","description":"Minimum 8 years of professional experience building analytical systems, developing statistical models, and applying data science to business problems in production environments."} - {"name":"Technical Mentorship","description":"Proven track record mentoring data scientists or quantitative analysts in industry settings, establishing analytical standards, and growing team capability."} - {"name":"Executive Communication","description":"Demonstrated success communicating technical findings to C-suite executives and cross-functional stakeholders to inform strategic and investment decisions."} - {"name":"Scalable Systems Building","description":"Experience evolving analytical work from ad-hoc analyses into production systems that deliver reliable insights and automated decision support repeatedly at scale."} ## Skills ### required - {"name":"SQL","description":"Expert-level proficiency in SQL for complex data querying, aggregation, and optimization across large-scale financial datasets and transactional systems."} - {"name":"Python","description":"Advanced Python programming for data manipulation, statistical analysis, machine learning model development, and production system implementation."} - {"name":"Statistical Analysis","description":"Deep expertise in experimental design, hypothesis testing, causal inference, and quantitative methods to validate analytical conclusions and inform strategic decisions."} - {"name":"Machine Learning","description":"Proficiency in building, training, and deploying ML models for classification, ranking, pricing optimization, risk assessment, and customer experience personalization."} - {"name":"Data Storytelling","description":"Ability to translate complex analytical findings into compelling narratives that influence executive strategy and cross-functional decision-making."} - {"name":"Technical Leadership","description":"Experience mentoring data scientists, code reviewing analytical work, establishing best practices, and defining team technical roadmaps in production environments."} - {"name":"Strategic Thinking","description":"Demonstrated ability to connect analytical insights to business strategy, identify high-impact problems, and influence organizational direction through data-driven reasoning."} - {"name":"Experimental Design","description":"Expertise in designing A/B tests, controlled experiments, and quasi-experimental approaches to isolate causal effects and measure incremental business impact."} ### preferred - {"name":"R","description":"Statistical computing experience for advanced analytical modeling and reproducible research practices."} - {"name":"FinTech Domain Knowledge","description":"Background in payments, banking, fraud detection, or financial services providing context for business-critical decision systems."} - {"name":"Large Language Models","description":"Experience working with LLMs, prompt engineering, or AI-powered analytics to build next-generation decision support systems."} - {"name":"Cloud Platforms","description":"Proficiency with AWS, GCP, or Azure for deploying scalable data science infrastructure and ML systems."} - {"name":"Product Analytics","description":"Track record of applying data science to optimize user engagement, conversion funnels, pricing strategies, or marketplace dynamics."} - {"name":"Governance and Risk","description":"Experience implementing model governance, bias detection, regulatory compliance frameworks, or risk assessment systems in regulated industries."} ## Tech stack ### tools - {"name":"Data Visualization","description":"Tableau, Looker, Plotly, or similar tools for creating executive dashboards and communicating analytical findings visually."} - {"name":"Jupyter/Notebooks","description":"Jupyter notebooks or equivalent for exploratory analysis, documentation, and communicating analytical methodology."} - {"name":"Version Control","description":"Git and GitHub for collaborative code development, code review, and maintaining analytical reproducibility."} - {"name":"Experimentation Platforms","description":"Experience with A/B testing platforms, experiment tracking tools, or building custom experimentation infrastructure."} ### others - {"name":"AI and Large Language Models","description":"Understanding of LLM capabilities, prompt engineering, and building AI-powered analytics and decision support systems."} - {"name":"Cloud Infrastructure","description":"Familiarity with AWS, GCP, or Azure for deploying scalable ML systems and orchestrating analytical workflows."} - {"name":"Governance and Compliance","description":"Knowledge of model governance, bias detection, regulatory compliance frameworks, and risk management in regulated industries."} ### databases - {"name":"Data Warehouses","description":"Experience with Snowflake, BigQuery, Redshift, or similar cloud data warehouse platforms supporting large-scale analytical queries."} - {"name":"Relational Databases","description":"PostgreSQL, MySQL, or Oracle for transactional data analysis and building analytical data models."} ### languages - {"name":"Python","description":"Primary programming language for data manipulation, statistical analysis, ML model development, and analytics automation in production systems."} - {"name":"SQL","description":"Essential for querying financial transaction data, aggregating metrics across large datasets, and optimizing analytical queries against data warehouses."} - {"name":"R","description":"Optional but valuable for statistical computing, advanced analytical methods, and creating reproducible analytical workflows."} ### frameworks - {"name":"Machine Learning Libraries","description":"scikit-learn, XGBoost, LightGBM, or similar frameworks for building classification, regression, and ranking models in the fintech domain."} - {"name":"Statistical Frameworks","description":"scipy, statsmodels, or equivalent libraries for hypothesis testing, causal inference, and rigorous quantitative analysis."} - {"name":"Data Analysis Tools","description":"pandas, NumPy, and similar tools for efficient data manipulation, transformation, and exploratory analysis at scale."} ## Benefits ### benefits - {"name":"Competitive Equity Package","description":"Participate in Airwallex's growth through stock options, aligning your financial success with company performance and shareholder value creation."} - {"name":"Hybrid Work Flexibility","description":"Work from Airwallex's London office three days per week with flexibility to work remotely, enabling work-life balance while maintaining team collaboration."} - {"name":"Global Career Opportunities","description":"Access to career growth across Airwallex's 27 offices spanning North America, Europe, Middle East, and Asia-Pacific, enabling international mobility and diverse project exposure."} - {"name":"Healthcare and Wellness","description":"Comprehensive health insurance, mental health support, and wellness programs supporting physical and mental wellbeing for you and your family."} - {"name":"Professional Development","description":"Opportunities to develop technical leadership skills, attend industry conferences, pursue advanced certifications, and work on cutting-edge AI and fintech challenges."} - {"name":"Inclusive Culture","description":"Work in a diverse, equitable environment where hiring and advancement decisions are based purely on merit, qualifications, and talent, with support for accommodations and special needs."} - {"name":"Impact-Driven Work","description":"Contribute to financial infrastructure serving 675,000+ businesses globally, making meaningful impact on how commerce operates in the modern economy."} - {"name":"Performance Bonus","description":"Eligibility for performance-based bonuses tied to individual and company objectives, rewarding achievement and contribution to organizational goals."} ## Compensation - **max:** 240000 - **min:** 180000 - **currency:** GBP - **stockOptions:** true ## Interview process ### steps - {"name":"Application and CV Review","description":"Submit your CV and cover letter through careers.airwallex.com. The hiring team will review your background, technical skills, mentorship experience, and track record of building scalable analytical systems."} - {"name":"Recruiter Screening Call","description":"A 30-minute conversation with the recruiter to discuss your experience, career trajectory, motivation for joining Airwallex, and role expectations. This is your opportunity to learn about the team and culture."} - {"name":"Technical Assessment","description":"A take-home project or live coding exercise evaluating your SQL, Python, and statistical analysis skills. You may be asked to solve a real-world analytical problem, demonstrating your ability to apply quantitative methods and communicate findings."} - {"name":"Hiring Manager Conversation","description":"A 45-60 minute discussion with the Data Science leader covering your experience mentoring teams, building scalable systems, influence on strategy, and your vision for evolving data science at Airwallex. Bring examples of your impact."} - {"name":"Cross-Functional Stakeholder Interview","description":"Meet with product, finance, or engineering leaders to assess how you collaborate across functions, translate technical findings into business language, and influence decision-making in a fast-paced environment."} - {"name":"Executive Leadership Discussion","description":"A conversation with senior leadership to discuss your strategic thinking, approach to ambiguity, alignment with Airwallex's operating principles, and long-term vision for the role and team."} - {"name":"Offer and Final Discussions","description":"If successful, receive a comprehensive offer including salary, equity, benefits, and work arrangement. Final conversations to address questions and prepare for your start date."} ## Full description ## **About Airwallex** Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own. We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our[ operating principles](https://www.airwallex.com/en-us/operating-principles) to see it in full. ## How you'll make impact As a Staff Data Scientist, you'll help Airwallex make better strategic and operational decisions through rigorous analysis, forecasting, and clear data storytelling. You'll combine hands-on analytical leadership with strategic influence, helping evolve data science into a more scalable, general capability across the business. * Uplevel the data science team through technical mentorship, hands-on leadership on complex analytical problems, and a clear vision for the team’s longer-term direction. * Act as a strategic thought partner to cross-functional teams, using data and technical expertise to inform, influence, and execute strategy and investment decisions. * Apply quantitative analysis, data mining, forecasting, and clear data storytelling to answer important business questions and guide strategic decisions. * Define goals, build forecasts, and monitor key business metrics to identify trends and assess performance across the organisation. * Help evolve data science from individual analyses into scalable systems of decision support, establishing robust analytical approaches that produce reliable insights repeatedly. You'll work from our London office three days a week. ## What we're looking for ### Essentials * 8+ years of industry experience. * An advanced degree, such as a PhD or MS, in a quantitative field such as Statistics, Engineering, Sciences, Computer Science, or Economics. * Experience communicating analytical results to leadership and cross-functional teams to influence strategy. * Experience providing technical mentorship in data science or quantitative analysis in an industry setting. * Expert-level ability in data-querying languages such as SQL, scripting languages such as Python, and/or statistical or mathematical software such as R. ### ## You'll thrive here if * You're comfortable owning the roadmap yourself. * You own the outcome and you don't wait for permission to fix what's broken. * You're comfortable with ambiguity. Give you a problem, not a prescription, and you'll run with it. * You connect dots across functions and hold the big picture without losing grip on the details. * You simplify complexity rather than hiding behind it. ## Learn more about your team Data Science at Airwallex is evolving from individual analyses and standalone models into scalable systems of decision and action. We bring together two complementary capabilities: DS Analytics, which improves decision quality and reduces decision latency, and DS Algorithm, which directly optimizes product and business outcomes in real time. You’ll build systems of decision that help teams understand what changed, determine what to do next, and learn whether the action worked. This means owning the AI analytics harness around the analysis—including semantic layers, context retrieval, evaluation infrastructure, validation gates, and governance—so decisions remain reliable, contextualized, and trustworthy at scale. You’ll also build systems of action that use machine learning and AI to shape product behavior, ranking, pricing, risk, allocation, and customer experiences. The work extends beyond training models to designing how intelligent systems reason, use tools, operate within guardrails, execute workflows, and measure their incremental impact. As AI makes ad hoc analysis increasingly accessible, the remaining advantage is scale, scientific method, and rigor. You’ll structure ambiguous problems, design experiments, identify confounding factors, challenge misleading conclusions, and build production systems that produce reliable outcomes repeatedly—not just one-off answers. The work is moving up the stack: from analysis to system, from model to harness, and from answering a question once to building something that answers it correctly over time. If you want to create data science systems that compound decision quality and turn intelligence into measurable action, this is where that work happens. ## **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.