Senior Data Scientist, Analytics (Risk & Fraud) Job at Airwallex (Shanghai)

Senior Data Scientist, Analytics (Risk & Fraud)

Airwallex is building a centralised risk platform that will harness advanced ana...

Location

Singapore , Singapore

Salary:

Not provided

Airwallex

Expiration Date

Until further notice

Requirements

Bachelor's degree in Mathematics, Statistics, Operations Research, Finance, Economics or related quantitative discipline
5+ years proven risk management experience or equivalent, including analytics, modeling implementation, and stakeholder management
5+ years of hands-on experience extracting and manipulating large data with SQL, experience with scripting in Python, Shell or R
Experience building visualizations using large datasets and multiple data sources using Looker or Tableau

Job Responsibility

Craft and refine risk strategies which include defining risk controls, customer experience and operational workflows, with emphasis on minimizing the impact on customers, our brand and organizational critical metrics
Use data driven insights to seize opportunity, measure performance, and determine what is working and what is not working. Monitor performance of existing & new solutions and optimize to ensure desired results
Communicate concise and actionable business strategies and present new strategy recommendations to senior management for approval
Collaborate with partners in Platform, Operations and other internal teams, as well as external partners like data vendors to formulate and execute fraud risk solutions

Fulltime

Data Scientist - Risk Analytics

We're looking for a Risk Analyst to join our Fraud & Financial Crime Team. You w...

Location

United Kingdom , London

Salary:

Not provided

Capital on Tap

Expiration Date

Until further notice

Requirements

At least three years of risk analysis experience ideally including credit cards, fraud or fincrime
Highly analytical and comfortable conducting complex analysis using SQL and Python
Exceptional detail orientated, problem solving skills and the ‘can do’ attitude to thrive in a fast paced, high growth environment
Excellent collaboration skills and a natural team player
Strong verbal communication skills including the ability to clearly explain complex concepts to other analysts, business stakeholders and senior leadership
2.1 or above at degree level, preferably in Economics or STEM subject

Job Responsibility

Owning, monitoring and continually optimising our fraud detection rules
Applying advanced analytics to maximise the efficiency and effectiveness of our Financial Crime detection processes
Supporting the launch of new products and balancing the need for robust fraud and financial crime controls whilst maintaining a positive customer experience
Working with external vendors to continually evaluate innovative or alternative products and tools
Proactively championing best use of AI to accelerate delivery of analytical output and as an integral part of our business solutions
Present and actively collaborate with a wide group of business stakeholders including Operations, Credit, Senior Management, and external business partners

What we offer

Private Healthcare including dental and opticians services through Vitality
Worldwide travel insurance through Vitality
Anniversary Rewards (£250, £500, £750, 4-week fully paid sabbatical)
Salary Sacrifice Pension Scheme up to 7% match
28 days holiday (plus bank holidays)
Annual Learning and Wellbeing Budget
Enhanced Parental Leave
Cycle to Work Scheme
Season Ticket Loan
6 free therapy sessions per year

Fulltime

Senior Data Scientist - Fraud Specialist

Are you looking for a new challenge? Fancy helping us shape the future of motor ...

Location

Spain , Madrid

Salary:

Not provided

Prima

Expiration Date

Until further notice

Requirements

Impressive academic achievements in Mathematics, Computer science, Physics, Statistics, Mathematical Engineering or Physical Engineering
2+ years of work/research experience in analytical fraud detection in the Insurance, Finance or Law Enforcement domain
Applied knowledge of network analysis, anomaly detection or risk scoring and segmentation using semi-supervised, self-supervised and human-in-the-loop machine learning techniques
Strong Python programming skills - especially in the data domain - and fluent use of GIT
Excellent English level, oral & written
Genuine, collaborative mindset that inspires and guides others

Job Responsibility

Design, build and evolve Prima’s proprietary predictive technology, in topics such as the prediction of future claims, tactical pricing, renewal probability, fraud detection and customer lifetime value, etc
Define and support the implementation in each country of the key tools to understand how the business is growing, for example: conversion, retention, emerging profitability. The monitoring tools need to be consistent at a global level and at the same time able to adapt to the different stages of evolution of each country
Support the countries in planning the pricing and UW strategy and consequent follow up
Develop a strong critical spirit and creativity that can act as an engine for Prima's growth

What we offer

Work Your Way: Enjoy full flexibility – work from home, the office or a mix of both. Plus, work from anywhere for up to 30 days a year
Grow with us: We may move fast at Prima, but we move together. Get access to learning resources, mentorship and a growth plan tailored to you
Thrive and perform: Your best work begins when you feel your best. Enjoy private healthcare, gym discounts, wellbeing programs and mental health support

Fulltime

Senior Data Scientist, AI & Model Risk

It all started with an idea at Block in 2013. Initially built to take the pain o...

Location

United States , Bay Area

Salary:

139000.00 - 225000.00 USD / Year

Cash App

Expiration Date

Until further notice

Requirements

A minimum of 5 years of related experience with a Bachelor’s degree in a quantitative field
or 3 years and a Master’s degree
or a PhD without experience
or equivalent work experience in risk management, model risk management, or AI risk management
Proficiency in Python or similar languages for evaluating AI system behavior, writing test scripts, or analyzing model outputs
Strong understanding of generative AI architectures
Including LLMs, transformer models, RAG systems, and agentic AI, plus hands-on experience interacting with and critically evaluating these systems, sufficient to assess design decisions, output quality, and limitations
Understanding of interagency model risk management principles, including SR 26-2
Knowledge of AI testing methodologies, ex. functional testing, bias testing, adversarial testing, and performance monitoring plus familiarity with data privacy and security principles (encryption, access controls, data classification)
Excellent written and verbal communication and the ability to translate complex technical AI concepts for non-technical stakeholders, senior management, and regulators

Job Responsibility

Lead end-to-end AI Risk Assessments for generative AI and LLM use cases across the Bank
Embedding in Block's enterprise-wide GenAI review process, coordinating cross-functional SMEs (Legal, Compliance, InfoSec, Data Governance, MRM, ERM, BRC, TPRM, Financial Crimes), and managing timelines to ensure reviews are completed within SLA
Review AI system design and documentation
Including retrieval sources, assumptions, limitations, fallback plans, guardrail configurations, and change management procedures — across banking use cases such as fraud detection, BSA/AML compliance, credit decisioning, and customer-facing applications, ensuring governance controls are commensurate with each use case's risk profile
Assess pre-deployment testing for adequacy inclusive of output integrity, hallucination detection, boundary and edge case testing, ethical and safety guardrails, bias testing, A/B testing, volume testing, and UAT — designing and conducting independent testing as needed
Evaluate ongoing monitoring plans for comprehensiveness - including accuracy, hallucination rates, drift detection, sensitive data controls, reliability metrics, CSAT, acceptable performance ranges, and documented remediation procedures
Develop and maintain templates, tools, and procedures to support the effectiveness and scalability of the AI Risk Governance Program
Monitor the evolving regulatory landscape for AI in banking — including FFIEC IT Examination Handbook standards, FDIC Financial Institution Letters, interagency statements, and the anticipated RFI on AI model risk management referenced in SR 26-2 — and incorporate emerging guidance into the AI risk governance program
support SFS's response to regulatory inquiries as needed

What we offer

Remote work
medical insurance
flexible time off
retirement savings plans
modern family planning

Fulltime

Senior Data Scientist, AI & Model Risk

It all started with an idea at Block in 2013. Initially built to take the pain o...

Location

United States , New York

Salary:

139000.00 - 225000.00 USD / Year

Cash App

Expiration Date

Until further notice

Requirements

A minimum of 5 years of related experience with a Bachelor’s degree in a quantitative field
or 3 years and a Master’s degree
or a PhD without experience
or equivalent work experience in risk management, model risk management, or AI risk management
Proficiency in Python or similar languages for evaluating AI system behavior, writing test scripts, or analyzing model outputs
Strong understanding of generative AI architectures
Including LLMs, transformer models, RAG systems, and agentic AI, plus hands-on experience interacting with and critically evaluating these systems, sufficient to assess design decisions, output quality, and limitations
Understanding of interagency model risk management principles, including SR 26-2
Knowledge of AI testing methodologies, ex. functional testing, bias testing, adversarial testing, and performance monitoring plus familiarity with data privacy and security principles (encryption, access controls, data classification)
Excellent written and verbal communication and the ability to translate complex technical AI concepts for non-technical stakeholders, senior management, and regulators

Job Responsibility

Lead end-to-end AI Risk Assessments for generative AI and LLM use cases across the Bank
Embedding in Block's enterprise-wide GenAI review process, coordinating cross-functional SMEs (Legal, Compliance, InfoSec, Data Governance, MRM, ERM, BRC, TPRM, Financial Crimes), and managing timelines to ensure reviews are completed within SLA
Review AI system design and documentation
Including retrieval sources, assumptions, limitations, fallback plans, guardrail configurations, and change management procedures — across banking use cases such as fraud detection, BSA/AML compliance, credit decisioning, and customer-facing applications, ensuring governance controls are commensurate with each use case's risk profile
Assess pre-deployment testing for adequacy inclusive of output integrity, hallucination detection, boundary and edge case testing, ethical and safety guardrails, bias testing, A/B testing, volume testing, and UAT — designing and conducting independent testing as needed
Evaluate ongoing monitoring plans for comprehensiveness - including accuracy, hallucination rates, drift detection, sensitive data controls, reliability metrics, CSAT, acceptable performance ranges, and documented remediation procedures
Develop and maintain templates, tools, and procedures to support the effectiveness and scalability of the AI Risk Governance Program
Monitor the evolving regulatory landscape for AI in banking — including FFIEC IT Examination Handbook standards, FDIC Financial Institution Letters, interagency statements, and the anticipated RFI on AI model risk management referenced in SR 26-2 — and incorporate emerging guidance into the AI risk governance program
support SFS's response to regulatory inquiries as needed

What we offer

Remote work
medical insurance
flexible time off
retirement savings plans
modern family planning

Fulltime

Senior Data Scientist

At myPOS, we’re all about helping businesses grow and get paid. We make payments...

Location

Bulgaria , Sofia

Salary:

Not provided

myPOS

Expiration Date

Until further notice

Requirements

MSc or PhD in Computer Science, Statistics, Applied Mathematics, Econometrics or a related quantitative field (or equivalent commercial experience)
7+ years of applied data science and ML experience in a commercial environment, with a strong portfolio of models in production that drove measurable business outcomes
Expert Python for data science and ML engineering: pandas, scikit-learn, XGBoost / LightGBM, PyTorch or TensorFlow
clean, tested, modular code as a default
Deep expertise across the ML methodological spectrum: survival analysis, time-series and sequence modelling, uplift and causal inference, anomaly detection, and recommendation systems
Proven end-to-end ownership of at least three of: CLTV models, churn models, propensity models, fraud/risk models, recommendation or NBA systems - in a production commercial setting
Strong MLOps capability: feature stores, model registries, model serving infrastructure, drift monitoring, and CI/CD for ML pipelines
Deep SQL and data platform proficiency (GCP / BigQuery strongly preferred)
experience with streaming architectures for real-time feature generation
Hands-on expertise building LLM-powered applications: RAG pipelines, tool-use agents, multi-agent orchestration, and agent evaluation frameworks

Job Responsibility

Own the full lifecycle of complex modelling programmes across Customer & Commercial Intelligence: CLTV, Churn Prediction, Propensity to Buy, and Next Most Likely Product (NMLP)
Architect multi-horizon churn models and build the churn intervention scoring layer that prioritises at-risk merchants for Account Management teams
Lead the NMLP engine — designing and productionizing a multi-output recommendation system that identifies the next product across myPOS's full catalogue
Take technical ownership of core fraud model components: transaction-level classifiers, merchant behaviour anomaly detectors, and new-account fraud scorers optimised for high-throughput, low-latency inference
Architect and own the Next Best Action (NBA) decisioning engine - a real-time system that selects the highest-expected-value action for each merchant at every interaction
Design and build production-grade agentic AI systems that automate high-value analytical and operational workflows
Define and execute experiment designs for online evaluation - A/B tests, uplift experiments, and bandits - and analyse results with statistical rigour
Set and enforce technical standards across the team: code quality, reproducibility, evaluation rigour, model documentation, and MLOps practices
Produce high-quality model documentation and present complex modelling work clearly to stakeholders across Sales, Marketing, Risk, Product, and Operations

What we offer

Excellent compensation package
25 days annual paid leave (+1 day per year up to 30)
Full "Luxury" package health insurance including dental care and optical glasses
Meal vouchers of 102.26 EUR per month
Fully covered Multisport card
Fully covered public transport pass for Sofia
Free coffee, snacks and drinks at the office
Annual salary reviews, promotions and performance bonuses
myPOS Academy for upskilling and training
Unlimited access to courses on LinkedIn Learning

Fulltime

Senior Data Scientist

Security represents the most critical priorities for our customers in a world aw...

Location

United States , Redmond

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft background and Microsoft Cloud background check upon hire/transfer and every two years thereafter

Job Responsibility

Design, develop, evaluate, and ship production ML models and applied AI systems (including LLM- and agent-based approaches) for compromise detection, fraud, and anti-abuse scenarios
Own detection problems end-to-end: problem framing, data exploration, feature engineering, modeling, offline/online evaluation, deployment, and post-launch monitoring
Analyze large-scale identity and security telemetry to identify attacker behavior, quantify risk, and uncover new detection opportunities
Define and run rigorous experiments (e.g., A/B tests, shadow evaluations) and establish quality, monitoring, and responsible-AI standards for the models you own
Partner with engineering to ensure models are scalable, reliable, observable, and maintainable in production
Collaborate with product management, security researchers, and partner teams to translate ambiguous security problems into clear metrics, roadmaps, and shipped solutions
Communicate insights, results, and tradeoffs clearly to technical and non-technical stakeholders, using strong data storytelling and visualization
Contribute to the technical growth of the team through design reviews, mentoring, and raising the engineering and science bar

Fulltime

Senior Data Scientist

As a Senior Data Scientist at myPOS, you will be the technical cornerstone of a ...

Location

Poland , Warsaw

Salary:

Not provided

myPOS

Expiration Date

Until further notice

Requirements

MSc or PhD in Computer Science, Statistics, Applied Mathematics, Econometrics or a related quantitative field (or equivalent commercial experience)
7+ years of applied data science and ML experience in a commercial environment, with a strong portfolio of models in production that drove measurable business outcomes
Expert Python for data science and ML engineering: pandas, scikit-learn, XGBoost / LightGBM, PyTorch or TensorFlow
clean, tested, modular code as a default
Deep expertise across the ML methodological spectrum: survival analysis, time-series and sequence modelling, uplift and causal inference, anomaly detection, and recommendation systems
Proven end-to-end ownership of at least three of: CLTV models, churn models, propensity models, fraud/risk models, recommendation or NBA systems - in a production commercial setting
Strong MLOps capability: feature stores, model registries, model serving infrastructure, drift monitoring, and CI/CD for ML pipelines
Deep SQL and data platform proficiency (GCP / BigQuery strongly preferred)
experience with streaming architectures for real-time feature generation
Hands-on expertise building LLM-powered applications: RAG pipelines, tool-use agents, multi-agent orchestration, and agent evaluation frameworks

Job Responsibility

Own the full lifecycle of complex modelling programmes across Customer & Commercial Intelligence: CLTV, Churn Prediction, Propensity to Buy, and Next Most Likely Product (NMLP)
Architect multi-horizon churn models and build the churn intervention scoring layer that prioritises at-risk merchants for Account Management teams
Lead the NMLP engine - designing and productionizing a multi-output recommendation system that identifies the next product across myPOS's full catalogue
Take technical ownership of core fraud model components: transaction-level classifiers, merchant behaviour anomaly detectors, and new-account fraud scorers optimised for high-throughput, low-latency inference
Architect and own the Next Best Action (NBA) decisioning engine - a real-time system that selects the highest-expected-value action for each merchant at every interaction
Design and build production-grade agentic AI systems that automate high-value analytical and operational workflows
Define and execute experiment designs for online evaluation - A/B tests, uplift experiments, and bandits - and analyse results with statistical rigour
Set and enforce technical standards across the team: code quality, reproducibility, evaluation rigour, model documentation, and MLOps practices
Produce high-quality model documentation and present complex modelling work clearly to stakeholders across Sales, Marketing, Risk, Product, and Operations

What we offer

Vibrant international team operating in hi-tech environment
Excellent compensation package
myPOS Academy for upskilling and training
Unlimited access to courses on LinkedIn Learning
Refer a friend bonus as we know that working with friends is fun
Teambuilding, social activities and networks on a multi-national level

Fulltime

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Senior Data Scientist, Analytics (Risk & Fraud)

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